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b/doc/htmldoc/synapses/connection_management.rst @@ -3,6 +3,74 @@ Connection Management ===================== +.. grid:: 4 + :gutter: 1 + + .. card:: + :link: one_to_one + :link-type: ref + + .. image:: ../static/img/One_to_one_H.png + + .. card:: + :link: all_to_all + :link-type: ref + + .. image:: ../static/img/All_to_all_H.png + + .. card:: + :link: fixed_indegree + :link-type: ref + + .. image:: ../static/img/Fixed_indegree_H.png + + .. card:: + :link: fixed_outdegree + :link-type: ref + + .. image:: ../static/img/Fixed_outdegree_H.png + +.. grid:: 4 + :gutter: 1 + + .. card:: + :link: fixed_total_number + :link-type: ref + + .. image:: ../static/img/Fixed_total_number_H.png + + .. card:: + :link: pairwise_bernoulli + :link-type: ref + + .. image:: ../static/img/Pairwise_bernoulli_H.png + + .. card:: + :link: multapse_autapse + :link-type: ref + + .. image:: ../static/img/Autapse_H.png + + .. card:: + :link: multapse_autapse + :link-type: ref + + .. image:: ../static/img/Multapse_H.png + + + +We here provide formal definitions of connectivity concepts for neuronal network models. These concepts encompass the basic connectivity rules illustrated above which are already commonly used by the computational neuroscience community. Beyond that, we discuss concepts to reflect some of the richness of anatomical brain connectivity and complement in particular non-spatial connectivity rules with rules for spatially organized connectivity. + +For each high-level connectivity rule, we give both an algorithmic construction rule and the resulting connectivity distribution. Modelers can use these definitions to succinctly specify connection rules in their studies. However, if details differ from our standard definitions, these details should still be specified. Furthermore, we suggest symbols that can be used to indicate the corresponding connectivity types in network diagrams and add the corresponding CSA expressions from [1]_. + +In the specification of connectivity concepts we use the following notations and definitions. Let :math:`\mathcal{S}=\{s_1,\ldots, s_{N_s}\}` be the ordered set of sources of cardinality :math:`N_s` and :math:`\mathcal{T}=\{t_1,\ldots, t_{N_t}\}` the set of targets of cardinality :math:`N_t`. Then the set of all possible directed edges between members of :math:`\mathcal{S}` and :math:`\mathcal{T}` is given by the Cartesian product :math:`\mathcal{E}_{ST}=\mathcal{S \times T}` of cardinality :math:`N_s\cdot N_t`. + +If the source and target populations are identical (:math:`\mathcal{S= T}`) a source can be its own target. We call such a self-connection an :ref:`autapse `. +If autapses are not allowed, the target set for any node :math:`i \in \mathcal{S}` is :math:`\mathcal{T=S} \setminus i`, with cardinality :math:`N_t=N_s-1`. +If there is more than one edge between a source and target (or from a node to itself), we call this a :ref:`multapse `. + +The :math:`{degree\ distribution}\ P(k)` is the distribution across nodes of the number of edges per node. In a directed network, the distribution of the number of edges going out of (into) a node is called the :math:`{out\!-\!degree} (in\!-\!degree)` distribution. The distributions given below describe the effect of applying a connection rule once to a given :math:`\mathcal{S}-\mathcal{T}` pair. + Connections between populations of neurons and between neurons and devices for stimulation and recording in NEST are created with the :py:func:`.Connect` function. Although each connection is internally @@ -62,8 +130,18 @@ done using the corresponding kernel attribute: Have a look at the :ref:`inspecting_connections` section further down to get more tips on how to examine the connections in greater detail. -Multapses and autapses ----------------------- + + + +.. _multapse_autapse: + +Autapses and multapses +---------------------------------------------- + +.. image:: ../static/img/Autapse_multapse.png + :width: 450px + :align: center + In the connection specification dictionary (containing the rule name and rule- specific parameters), the additional switch ``allow_autapses`` (default: @@ -77,904 +155,410 @@ These switches are only effective during each single call to neurons might still lead to violations of these constraints, even though the switches were set to `False` in each individual call. -.. _conn_rules: -Connection Rules ----------------- -Connection rules are specified using the ``conn_spec`` parameter of -:py:func:`.Connect`, which can be either just a string naming a connection -rule, or a dictionary containing a rule specification. Only connection -rules requiring no parameters can be given as strings, for all other -rules, a dictionary specifying the rule and its parameters is -required. Examples for such parameters might be in- and out-degrees, -or the probability for establishing a connection. A description of -all connection rules available in NEST is available below. +Deterministic connectivity rules +------------------------------------------------- +Deterministic connectivity rules establish precisely defined sets of connections without any variability across network realizations. -all-to-all +.. _one_to_one: + +One-to-one ~~~~~~~~~~ -.. image:: ../static/img/All_to_all.png +.. image:: ../static/img/One_to_one.png :width: 200px :align: center -Each node in ``A`` is connected to every node in ``B``. Since -``all_to_all`` is the default, the rule doesn't actually have to be -specified. +The `i`\-th node in ``S`` (source) is connected to the `i`\-th node in ``T`` (target). The +NodeCollections of ``S`` and ``T`` have to contain the same number of +nodes. .. code-block:: python - n, m = 10, 12 - A = nest.Create('iaf_psc_alpha', n) - B = nest.Create('iaf_psc_alpha', m) - nest.Connect(A, B, 'all_to_all') - nest.Connect(A, B) # equivalent + n = 5 + S = nest.Create('iaf_psc_alpha', n) + T = nest.Create('spike_recorder', n) + nest.Connect(S, T, 'one_to_one') -.. _conn_builder_conngen: - -conngen -~~~~~~~ - -.. admonition:: Availability - - This connection rule is only available if NEST was compiled with - :ref:`support for libneurosim `. +.. dropdown:: Mathematical details: One-to-one -To allow the generation of connectivity by means of an external -library, NEST supports the Connection Generator Interface [1]_. For -more details on this interface, see the Git repository of `libneurosim -`_. + | **Symbol:** :math:`\delta` + | **CSA:** :math:`\delta` + | **Definition:** Each node in :math:`\mathcal{S}` is uniquely connected to one node in :math:`\mathcal{T}`. + | :math:`\mathcal{S}` and :math:`\mathcal{T}` must have identical cardinality :math:`N_s=N_t`, see :ref:`one to one `. Both sources and targets can be permuted independently even if :math:`\mathcal{S}=\mathcal{T}`. The in- and out-degree distributions are given by :math:`P(K)=\delta_{K,1}`, with Kronecker delta :math:`\delta_{i,j}=1` if :math:`i=j`, and zero otherwise. -In contrast to the other rules for creating connections, this rule -relies on a Connection Generator object to describe the connectivity -pattern in a library-specific way. The Connection Generator is handed -to :py:func:`.Connect` under the key ``cg`` of the connection specification -dictionary and evaluated internally. If the Connection Generator -provides values for connection weights and delays, their respective -indices can be specified under the key ``params_map``. Alternatively, -all synapse parameters can be specified using the synapse -specification argument to ``Connect()``. -The following listing shows an example for using the `Connection-Set -Algebra `_ in NEST via the Connection -Generator Interface and randomly connects 10% of the neurons from -``A`` to the neurons in ``B``, each connection having a weight of -10000.0 pA and a delay of 1.0 ms: +.. _all_to_all: -.. code-block:: python - - A = nest.Create('iaf_psc_alpha', 100) - B = nest.Create('iaf_psc_alpha', 100) - - # Create the Connection Generator object - import csa - cg = csa.cset(csa.random(0.1), 10000.0, 1.0) - - # Map weight and delay indices to values from cg - params_map = {'weight': 0, 'delay': 1} - - conn_spec_dict = {'rule': 'conngen', 'cg': cg, 'params_map': params_map} - nest.Connect(A, B, conn_spec_dict) - -fixed indegree -~~~~~~~~~~~~~~ +All-to-all +~~~~~~~~~~ -.. image:: ../static/img/Fixed_indegree.png +.. image:: ../static/img/All_to_all.png :width: 200px :align: center -The nodes in ``A`` are randomly connected with the nodes in ``B`` such -that each node in ``B`` has a fixed :hxt_ref:`indegree` of ``N``. +Each node in ``S`` is connected to every node in ``T``. Since +``all_to_all`` is the default, the rule doesn't actually have to be +specified. .. code-block:: python - n, m, N = 10, 12, 2 - A = nest.Create('iaf_psc_alpha', n) - B = nest.Create('iaf_psc_alpha', m) - conn_spec_dict = {'rule': 'fixed_indegree', 'indegree': N} - nest.Connect(A, B, conn_spec_dict) - -fixed outdegree -~~~~~~~~~~~~~~~ - -.. image:: ../static/img/Fixed_outdegree.png - :width: 200px - :align: center - -The nodes in ``A`` are randomly connected with the nodes in ``B`` such -that each node in ``A`` has a fixed :hxt_ref:`outdegree` of ``N``. + n, m = 5, 5 + S = nest.Create('iaf_psc_alpha', n) + T = nest.Create('iaf_psc_alpha', m) + nest.Connect(S, T, 'all_to_all') + nest.Connect(S, T) # equivalent -.. code-block:: python +.. dropdown:: Mathematical details: All-to-all - n, m, N = 10, 12, 2 - A = nest.Create('iaf_psc_alpha', n) - B = nest.Create('iaf_psc_alpha', m) - conn_spec_dict = {'rule': 'fixed_outdegree', 'outdegree': N} - nest.Connect(A, B, conn_spec_dict) + | **Symbol:** :math:`\Omega` + | **CSA:** :math:`\Omega` + | **Definition:** Each node in :math:`\mathcal{S}` is connected to all nodes in :math:`\mathcal{T}`. + | The resulting edge set is the full edge set :math:`\mathcal{E}_\mathcal{ST}`. The in- and out-degree distributions are :math:`P_\text{in}(K)=\delta_{K,N_s}` for :math:`\mathcal{T}`, and :math:`P_\text{out}(K)=\delta_{K,N_t}` for :math:`\mathcal{S}`, respectively. An example is shown in :ref:`all to all `. -fixed total number -~~~~~~~~~~~~~~~~~~ +.. dropdown:: Mathematical details: Explicit connections -The nodes in ``A`` are randomly connected with the nodes in ``B`` -such that the total number of connections equals ``N``. + | **Symbol:** X + | **CSA:** Not applicable + | **Definition:** Connections are established according to an explicit list of source-target pairs. + | Connectivity is defined by an explicit list of sources and targets, also known as *adjacency list*, as for instance derived from anatomical measurements. It is, hence, not the result of any specific algorithm. An alternative way of representing a fixed connectivity is by means of the *adjacency matrix A*, such that :math:`A_{ij}=1` if :math:`j` is connected to :math:`i`, and zero otherwise. We here adopt the common computational neuroscience practice to have the first index :math:`i` denote the target and the second index :math:`j` denote the source node. -.. code-block:: python +Probabilistic connectivity rules +------------------------------------------------ +Probabilistic connectivity rules establish edges according to a probabilistic rule. Consequently, the exact connectivity varies with realizations. Still, such connectivity leads to specific expectation values of network characteristics, such as degree distributions or correlation structure. - n, m, N = 10, 12, 30 - A = nest.Create('iaf_psc_alpha', n) - B = nest.Create('iaf_psc_alpha', m) - conn_spec_dict = {'rule': 'fixed_total_number', 'N': N} - nest.Connect(A, B, conn_spec_dict) +.. _pairwise_bernoulli: -one-to-one -~~~~~~~~~~ +Pairwise bernoulli +~~~~~~~~~~~~~~~~~~ -.. image:: ../static/img/One_to_one.png +.. image:: ../static/img/Pairwise_bernoulli.png :width: 200px :align: center -The `i`\ th node in ``A`` is connected to the `i`\ th node in ``B``. The -NodeCollections of ``A`` and ``B`` have to contain the same number of -nodes. +For each possible pair of nodes from ``S`` and ``T``, a connection is +created with probability ``p``. .. code-block:: python - n = 10 - A = nest.Create('iaf_psc_alpha', n) - B = nest.Create('spike_recorder', n) - nest.Connect(A, B, 'one_to_one') + n, m, p = 5, 5, 0.5 + S= nest.Create('iaf_psc_alpha', n) + T = nest.Create('iaf_psc_alpha', m) + conn_spec_dict = {'rule': 'pairwise_bernoulli', 'p': p} + nest.Connect(S, T, conn_spec_dict) + +.. dropdown:: Mathematical details: Pairwise bernoulli -pairwise bernoulli -~~~~~~~~~~~~~~~~~~ + | **Symbol:** :math:`p` + | **CSA:** :math:`\rho(p)` + | **Definition:** Each pair of nodes, with source in :math:`\mathcal{S}` and target in :math:`\mathcal{T}`, is connected with probability :math:`p`. + | In its standard form this rule cannot produce multapses since each possible edge is visited only once. If :math:`\mathcal{S=T}`, this concept is similar to Erdős-Rényi-graphs of the *constant probability* :math:`p-ensemble\ G(N,p)`---also called *binomial ensemble* [2]_; the only difference being that we here consider directed graphs, whereas the Erdős-Rényi model is undirected. The distribution of both in- and out-degrees is binomial, -For each possible pair of nodes from ``A`` and ``B``, a connection is -created with probability ``p``. + .. math:: + P(K_\text{in}=K)=\mathcal{B}(K|N_s,p):=\begin{pmatrix}N_s\\K\end{pmatrix}p^{K}(1-p)^{N_s-K} -.. code-block:: python + and - n, m, p = 10, 12, 0.2 - A = nest.Create('iaf_psc_alpha', n) - B = nest.Create('iaf_psc_alpha', m) - conn_spec_dict = {'rule': 'pairwise_bernoulli', 'p': p} - nest.Connect(A, B, conn_spec_dict) + .. math:: + P(K_\text{out}=K)=\mathcal{B}(K|N_t,p)\,, + + respectively. + The expected total number of edges equals :math:`\text{E}[N_\text{syn}]=pN_tN_s`. -symmetric pairwise bernoulli +Symmetric pairwise bernoulli ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -For each possible pair of nodes from ``A`` and ``B``, a connection is -created with probability ``p`` from ``A`` to ``B``, as well as a -connection from ``B`` to ``A`` (two connections in total). To use +For each possible pair of nodes from ``S`` and ``T``, a connection is +created with probability ``p`` from ``S`` to ``T``, as well as a +connection from ``T`` to ``S`` (two connections in total). To use this rule, ``allow_autapses`` must be ``False``, and ``make_symmetric`` must be ``True``. .. code-block:: python n, m, p = 10, 12, 0.2 - A = nest.Create('iaf_psc_alpha', n) - B = nest.Create('iaf_psc_alpha', m) + S = nest.Create('iaf_psc_alpha', n) + T = nest.Create('iaf_psc_alpha', m) conn_spec_dict = {'rule': 'symmetric_pairwise_bernoulli', 'p': p, 'allow_autapses': False, 'make_symmetric': True} - nest.Connect(A, B, conn_spec_dict) - -.. _synapse_spec: - -Synapse Specification ---------------------- - -The synapse properties can be given as just the name of the desired -synapse model as a string, as a dictionary specifying synapse model -and parameters, or as a ``CollocatedSynapse`` object creating -multiple synapses for each source-target pair as detailed in -the section on :ref:`collocated synapses `. - -.. code-block:: python - - n = 10 - A = nest.Create('iaf_psc_alpha', n) - B = nest.Create('iaf_psc_alpha', n) - nest.Connect(A, B, syn_spec='static_synapse') - - syn_spec_dict = {'synapse_model': 'stdp_synapse', - 'weight': 2.5, 'delay': 0.5} - nest.Connect(A, B, syn_spec=syn_spec_dict) - -If synapse properties are given as a dictionary, it may include the keys -``synapse_model`` (default `static_synapse`), ``weight`` (default 1.0), -``delay`` (default 1.0), and ``receptor_type`` (default 0, see :ref:`receptor-types` for details), -as well as parameters specific to the chosen synapse model. The specification of -all parameters is optional, and unspecified parameters will take on the -default values of the chosen synapse model that can be inspected using -``nest.GetDefaults(synapse_model)``. - -Parameters can be either :ref:`fixed scalar values `, -:ref:`arrays of values `, or :ref:`expressions `. - -One synapse dictionary can contain an arbitrary combination of parameter types, -as long as they are supported by the chosen connection rule. - - -.. _scalar_params: - -Scalar parameters -~~~~~~~~~~~~~~~~~ - -Scalar parameters must be given with the correct type. The ``weight`` -for instance must be a float, while the ``receptor_type`` has to be of -type integer. When a synapse parameter is given as a scalar, the value -will be applied to all connections created in the current -:py:func:`.Connect` call. - -.. code-block:: python - - n = 10 - neuron_dict = {'tau_syn': [0.3, 1.5]} - A = nest.Create('iaf_psc_exp_multisynapse', n, neuron_dict) - B = nest.Create('iaf_psc_exp_multisynapse', n, neuron_dict) - syn_spec_dict ={'synapse_model': 'static_synapse', 'weight': 2.5, 'delay': 0.5, 'receptor_type': 1} - nest.Connect(A, B, syn_spec=syn_spec_dict) - -.. _array_params: - -Array parameters -~~~~~~~~~~~~~~~~ - -Array parameters can be used with the rules ``all_to_all``, -``fixed_indegree``, ``fixed_outdegree``, ``fixed_total_number`` and -``one_to_one``. The arrays can be specified as NumPy arrays or Python -lists. As with the scalar parameters, all parameters have to be -specified as arrays of the correct type. - -rule: all-to-all -^^^^^^^^^^^^^^^^ - -When connecting with rule ``all_to_all``, the array parameter must -have dimension `len(B) x len(A)`. - -.. code-block:: python - - A = nest.Create('iaf_psc_alpha', 3) - B = nest.Create('iaf_psc_alpha', 2) - syn_spec_dict = {'weight': [[1.2, -3.5, 2.5], [0.4, -0.2, 0.7]]} - nest.Connect(A, B, syn_spec=syn_spec_dict) - -rule: fixed indegree -^^^^^^^^^^^^^^^^^^^^ - -For rule ``fixed_indegree`` the array has to be a two-dimensional -NumPy array or Python list with shape ``(len(B), indegree)``, where -:hxt_ref:`indegree` is the number of incoming connections per target neuron. -This means that the rows describe the target, while the columns -represent the connections converging on the target neuron, regardless -of the identity of the source neurons. - -.. code-block:: python - - A = nest.Create('iaf_psc_alpha', 5) - B = nest.Create('iaf_psc_alpha', 3) - conn_spec_dict = {'rule': 'fixed_indegree', 'indegree': 2} - syn_spec_dict = {'weight': [[1.2, -3.5],[0.4, -0.2],[0.6, 2.2]]} - nest.Connect(A, B, conn_spec_dict, syn_spec_dict) - -rule: fixed outdegree -^^^^^^^^^^^^^^^^^^^^^ - -For rule ``fixed_outdegree`` the array has to be a two-dimensional -NumPy array or Python list with shape ``(len(A), outdegree)``, where -:hxt_ref:`outdegree` is the number of outgoing connections per source -neuron. This means that the rows describe the source, while the -columns represent the connections starting from the source neuron -regardless of the identity of the target neuron. - -.. code-block:: python - - A = nest.Create('iaf_psc_alpha', 2) - B = nest.Create('iaf_psc_alpha', 5) - conn_spec_dict = {'rule': 'fixed_outdegree', 'outdegree': 3} - syn_spec_dict = {'weight': [[1.2, -3.5, 0.4], [-0.2, 0.6, 2.2]]} - nest.Connect(A, B, conn_spec_dict, syn_spec_dict) - -rule: fixed total number -^^^^^^^^^^^^^^^^^^^^^^^^ - -For rule ``fixed_total_number``, the array has to be same the length as the -number of connections ``N``. - -.. code-block:: python - - A = nest.Create('iaf_psc_alpha', 3) - B = nest.Create('iaf_psc_alpha', 4) - conn_spec_dict = {'rule': 'fixed_total_number', 'N': 4} - syn_spec_dict = {'weight': [1.2, -3.5, 0.4, -0.2]} - nest.Connect(A, B, conn_spec_dict, syn_spec_dict) - -rule: one-to-one -^^^^^^^^^^^^^^^^ - -For rule ``one_to_one`` the array must have the same length as there -are nodes in ``A`` and ``B``. - -.. code-block:: python - - A = nest.Create('iaf_psc_alpha', 2) - B = nest.Create('spike_recorder', 2) - conn_spec_dict = {'rule': 'one_to_one'} - syn_spec_dict = {'weight': [1.2, -3.5]} - nest.Connect(A, B, conn_spec_dict, syn_spec_dict) - -.. _dist_params: - -Expressions as parameters -~~~~~~~~~~~~~~~~~~~~~~~~~ - -``nest.Parameter`` objects support a flexible specification of synapse -parameters through expressions. This includes parameters drawn from random -distributions and -depending on spatial properties of source and target neurons. Parameters -can be combined through mathematical expressions including conditionals, -providing for a high degree of flexibility. - -The following parameters and functionalities are provided: - -- Random parameters -- Spatial parameters -- Spatially distributed parameters -- Mathematical functions -- Clipping, redrawing, and conditional parameters - -For more information, check out the guide on -:ref:`parametrization ` or the documentation on the -different :ref:`PyNEST APIs `. - -.. code-block:: python - - n = 10 - A = nest.Create('iaf_psc_alpha', n) - B = nest.Create('iaf_psc_alpha', n) - syn_spec_dict = { - 'synapse_model': 'stdp_synapse', - 'weight': 2.5, - 'delay': nest.random.uniform(min=0.8, max=2.5), - 'alpha': nest.math.redraw(nest.random.normal(mean=5.0, std=1.0), min=0.5, max=10000.) - } - nest.Connect(A, B, syn_spec=syn_spec_dict) - -In this example, the default connection rule ``all_to_all`` is used -and connections will be using synapse model :hxt_ref:`stdp_synapse`. All synapses -are created with a static weight of 2.5 and a delay that is uniformly -distributed in [0.8, 2.5]. The parameter ``alpha`` is drawn from a -normal distribution with mean 5.0 and standard deviation 1.0; -values below 0.5 and above 10000 are excluded by re-drawing if they should occur. -Thus, the actual distribution is a slightly distorted Gaussian. - -If the synapse type is supposed to have a unique name and still use -distributed parameters, it needs to be defined in two steps: - -.. code-block:: python - - n = 10 - A = nest.Create('iaf_psc_alpha', n) - B = nest.Create('iaf_psc_alpha', n) - nest.CopyModel('stdp_synapse', 'excitatory', {'weight':2.5}) - syn_dict = { - 'synapse_model': 'excitatory', - 'weight': 2.5, - 'delay': nest.random.uniform(min=0.8, max=2.5), - 'alpha': nest.math.redraw(nest.random.normal(mean=5.0, std=1.0), min=0.5, max=10000.) - } - nest.Connect(A, B, syn_spec=syn_dict) - -For further information on the available distributions see -:ref:`Random numbers in NEST `. - -.. _collocated_synapses: - -Collocated synapses -~~~~~~~~~~~~~~~~~~~ - -Some modeling applications require multiple connections between the -same pairs of nodes. An example of this could be a network, where each -pre-synaptic neuron connects with a static synapse to a modulatory -receptor on the post-synaptic neuron and with a plastic synapse to a -normal NMDA-type receptor. - -This type of connectivity is especially hard to realize when using -randomized connection rules, as the chosen pairs that are actually -connected are only known internally, and have to be retrieved manually -after the call to :py:func:`.Connect` returns. - -To ease the setup of such connectivity patterns, NEST supports a -concept called `collocated synapses`. This allows the creation of several -connections between chosen pairs of neurons (possibly with different -synapse types or parameters) in a single call to ``nest.Connect()``. - -To create collocated synapses, the synapse specification consists of -an object of type ``CollocatedSynapses``, whose constructor takes -synapse specification dictionaries as arguments and applies the given -dictionaries to each source-target pair internally. - -.. code-block:: python - - nodes = nest.Create('iaf_psc_alpha', 3) - syn_spec = nest.CollocatedSynapses({'weight': 4.0, 'delay': 1.5}, - {'synapse_model': 'stdp_synapse'}, - {'synapse_model': 'stdp_synapse', 'alpha': 3.0}) - nest.Connect(nodes, nodes, conn_spec='one_to_one', syn_spec=syn_spec) - print(nest.GetConnections().alpha) - -The example above will create 9 connections in total because there are -3 neurons times 3 synapse specifications in the :py:func:`.CollocatedSynapses` -object, and the connection rule ``one_to_one`` is used. - - -.. code-block:: python - - >>> print(nest.num_connections) - 9 + nest.Connect(S, T, conn_spec_dict) -In more detail, the connections have the following properties: +.. _fixed_total_number: -* 3 are of type :hxt_ref:`static_synapse` with `weight` 4.0 and `delay` 1.5 -* 3 are of type :hxt_ref:`stdp_synapse` with the default value for `alpha` -* 3 are of type :hxt_ref:`stdp_synapse` with an `alpha` of 3.0. -If you want to connect with different :ref:`receptor types -`, you can do the following: -.. code-block:: python - - A = nest.Create('iaf_psc_exp_multisynapse', 7) - B = nest.Create('iaf_psc_exp_multisynapse', 7, {'tau_syn': [0.1 + i for i in range(7)]}) - syn_spec_dict = nest.CollocatedSynapses({'weight': 5.0, 'receptor_type': 2}, - {'weight': 1.5, 'receptor_type': 7}) - nest.Connect(A, B, 'one_to_one', syn_spec_dict) - -.. code-block:: python - - >>> print(nest.GetConnections().get()) - -You can see how many synapse parameters you have by calling ``len()`` on -your ``CollocatedSynapses`` object: - -.. code-block:: python - - >>> len(syn_spec_dict) - 2 - -Spatially-structured networks ------------------------------ - -Nodes in NEST can be created so that they have a position in two- or -three-dimensional space. To take full advantage of the arrangement of -nodes, connection parameters can be based on the nodes' positions or -their spatial relation to each other. See :ref:`Spatially-structured -networks ` for the full information about how to create -and connect such networks. - -Connecting sparse matrices with array indexing ----------------------------------------------- - -Oftentimes, you will find yourself in a situation, where you want to -base your connectivity on actual data instead of rules. A common -scenario is that you have a (sometimes sparse) connection matrix -coming from an experiment or from a graph algorithm. Let's assume you -have a weight matrix of the form: - -.. math:: - - W = \begin{bmatrix} - w_{11} & w_{21} & \cdots & w_{n1} \\ - w_{12} & w_{22} & \cdots & w_{n2} \\ - \vdots & \vdots & \ddots & \vdots \\ - w_{1m} & w_{2m} & \cdots & w_{nm} \\ - \end{bmatrix} - -where :math:`w_{ij}` is the weight of the connection with pre-synaptic -node :math:`i` and post-synaptic node :math:`j`. In all generality, we -can assume that some weights are zero, indicating that there is no -connection at all. - -As there is no support for creating connections from the whole matrix -directly, we will instead just iterate the pre-synaptic neurons and -connect one column at a time. We assume that there are :math:`n` -pre-synaptic nodes in the NodeCollection ``A`` and :math:`m` -post-synaptic nodes in ``B``. We also assume that we have our weight -matrix given as a two-dimensional NumPy array `W`, with :math:`n` -columns and :math:`m` rows. - -.. code-block:: python +Random, fixed total number +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - W = numpy.array([[0.5, 0.0, 1.5], - [1.3, 0.2, 0.0], - [0.0, 1.25, 1.3]]) - - A = nest.Create('iaf_psc_alpha', 3) - B = nest.Create('iaf_psc_alpha', 3) - - for i, pre in enumerate(A): - # Extract the weights column. - weights = W[:, i] - - # To only connect pairs with a nonzero weight, we use array indexing - # to extract the weights and post-synaptic neurons. - nonzero_indices = numpy.where(weights != 0)[0] - weights = weights[nonzero_indices] - post = B[nonzero_indices] - - # Generate an array of node IDs for the column of the weight - # matrix, with length based on the number of nonzero - # elements. The array's dtype must be an integer. - pre_array = numpy.ones(len(nonzero_indices), dtype=int) * pre.get('global_id') - - # nest.Connect() automatically converts post to a NumPy array - # because pre_array contains multiple identical node IDs. When - # also specifying a one_to_one connection rule, the arrays of - # node IDs can then be connected. - nest.Connect(pre_array, post, conn_spec='one_to_one', syn_spec={'weight': weights}) - -.. _receptor-types: - -Receptor Types --------------- - -Conceptually, each connection in NEST terminates at a `receptor` on -the target node. The exact meaning of such a receptor depends on the -concrete type of that node. In a multi-compartment neuron, for -instance, the different compartments could be addressed as different -receptors, while another neuron model might make sets of different -synaptic parameters available for each receptor. Please refer to the -:doc:`model documentation <../models/index_neuron>` for details. - -In order to connect a pre-synaptic node to a certain receptor on a -post-synaptic node, the integer ID of the target receptor can be -supplied under the key ``receptor_type`` in the ``syn_spec`` -dictionary during the call to :py:func:`.Connect`. If unspecified, the -receptor will take on its default value of 0. If you request a -receptor that is not available in the target node, this will result in -a runtime error. - -To illustrate the concept of receptors in more detail, the following -example shows how to connect several ``iaf_psc_alpha`` neurons to the -different compartments of a multi-compartment integrate-and-fire -neuron (``iaf_cond_alpha_mc``) that are represented by different -receptors. - -.. image:: ../static/img/Receptor_types.png +.. image:: ../static/img/Fixed_total_number.png :width: 200px :align: center -.. code-block:: python - - A1 = nest.Create('iaf_psc_alpha') - A2 = nest.Create('iaf_psc_alpha') - A3 = nest.Create('iaf_psc_alpha') - A4 = nest.Create('iaf_psc_alpha') - B = nest.Create('iaf_cond_alpha_mc') - - receptors = nest.GetDefaults('iaf_cond_alpha_mc')['receptor_types'] - -.. code-block:: python - - >>> print(receptors) - {'soma_exc': 1, - 'soma_inh': 2, - 'soma_curr': 7, - 'proximal_exc': 3 - 'proximal_inh': 4, - 'proximal_curr': 8, - 'distal_exc': 5, - 'distal_inh': 6, - 'distal_curr': 9,} - -.. code-block:: python - - nest.Connect(A1, B, syn_spec={'receptor_type': receptors['distal_inh']}) - nest.Connect(A2, B, syn_spec={'receptor_type': receptors['proximal_inh']}) - nest.Connect(A3, B, syn_spec={'receptor_type': receptors['proximal_exc']}) - nest.Connect(A4, B, syn_spec={'receptor_type': receptors['soma_inh']}) - -In the example above, we retrieve a map of available receptors and -their IDs by extracting the `receptor_types` property from the model -defaults. This functionality is, however, only available for models -with a predefined number of receptors, while models with a variable -number of receptors usually don't provide such an enumeration. -An example for the latter are the `*_multisynapse` neuron models that -support multiple individual synaptic time constants for the different -receptors. In these models, the number of available receptors is not -predefined, but determined only by the length of the ``tau_syn`` -vector that is supplied to the model instance. The following example -shows the setup and connection of such a model in more detail: +The nodes in ``S`` are randomly connected with the nodes in ``T`` +such that the total number of connections equals ``N``. .. code-block:: python - A = nest.Create('iaf_psc_alpha') - B = nest.Create('iaf_psc_exp_multisynapse', params={'tau_syn': [0.1, 0.2, 0.3]}) - - print(B.n_synapses) # This will print 3, as we set 3 different tau_syns - - nest.Connect(A, B, syn_spec={'receptor_type': 2}) - - -.. _synapse-types: - -Synapse Types -------------- - -NEST provides a number of built-in synapse models that can be used -during connection setup. The default model is the :hxt_ref:`static_synapse`, -whose only parameters ``weight`` and ``delay`` do not change over -time. Other synapse models implement learning and adaptation in the -form of long-term or short-term plasticity. A list of available -synapse models is accessible via the command ``nest.synapse_models``. -More detailed information about each of them can be found in the -:doc:`model directory <../models/index_synapse>`. - -.. note:: - Not all nodes can be connected via all available synapse types. The - events a synapse type is able to transmit is documented in the - ``Transmits`` section of the model documentation. - -All synapses store their parameters on a per-connection basis. -However, each of the built-in models is registered with the simulation -kernel in a number of different ways that slightly modify the -available properties of the connections instantiated from the model. -The different variants are indicated by specific suffixes: + n, m, N = 5, 5, 10 + S = nest.Create('iaf_psc_alpha', n) + T = nest.Create('iaf_psc_alpha', m) + conn_spec_dict = {'rule': 'fixed_total_number', 'N': N} + # By default multapses are allowed, + # optionally if multapses are not allowed the following parameter must be set + # conn_spec_dict = {'rule': 'fixed_total_number', 'N': N, 'allow_autapses': False} + nest.Connect(S, T, conn_spec_dict) -.. glossary:: +.. dropdown:: Mathematical details: Random, fixed total number without multapses - ``_lbl`` - denotes `labeled synapses` that have an additional parameter - `synapse_label` (type: int), which can be set to a user-defined - value. In a common application this label is used to store an - additional projection identifier. Please note that using this - synapse variant may drive up the memory requirements of your - simulations significantly, as the label is stored on a - `per-synapse` basis. + | **Symbol:** :math:`N_\text{syn} \cancel{M}` + | **CSA:** :math:`\mathbf{\rho_{N}}(N_\text{syn})(\mathbb{N}_S \times \mathbb{N}_T)` + | **Definition:** :math:`N_\text{syn}\in\{0,\ldots,N_sN_t\}` edges are randomly drawn from the edge set :math:`\mathcal{E}_\mathcal{ST}` without replacement. + | For :math:`\mathcal{S}=\mathcal{T}` this is a directed graph generalization of Erdős-Rényi graphs of the *constant number of edges* :math:`N_\text{syn}`-ensemble :math:`G(N,N_\text{syn})` [3]_. There are :math:`\begin{pmatrix}N_s N_t\\N_\text{syn}\end{pmatrix}` possible networks for any given number :math:`N_\text{syn}\leq N_sN_t`, which all have the same probability. The resulting in- and out-degree distributions are multivariate hypergeometric distributions. - ``_hpc`` - denotes `synapses for high-performance computing scenarios`, which - have minimal memory requirements by using thread-local target node - indices internally. Use this version if you are running very large - simulations. + .. math:: + \begin{split} + &P(K_{\text{in},1}=K_1,\ldots,K_{\text{in},N_t}=K_{N_t})\\ + & \quad \quad \quad = \begin{cases} + \prod_{j=1}^{N_t} \begin{pmatrix} N_s\\K_j\end{pmatrix}\Bigg/\begin{pmatrix} N_sN_t\\N_\text{syn}\end{pmatrix} + & \text{if}\,\,\sum_{j=1}^{N_t} K_j = N_\text{syn}\\ + \phantom{bl}0 & \text{otherwise}\end{cases}\,, + \end{split} - ``_hom`` - denotes `homogeneous synapses` that store certain parameters like - `weight` and `delay` only once for all synapses of the same type - and can thus be used to save memory. + and analogously :math:`P(K_{\text{out},1}=K_1,\ldots,K_{\text{out},N_s}=K_{N_s})` + with :math:`K_\text{out}` instead of :math:`K_\text{in}` and source and target indices switched. -The default parameter values of a synapse model can be inspected using -the command :py:func:`.GetDefaults`, which only takes the name of the -synapse model as an argument and returns a dictionary. Likewise, the -function :py:func:`.SetDefaults` takes the name of a synapse type and a -parameter dictionary as arguments and will modify the defaults of the -given model. + The marginal distributions, i.e., the probability distribution for any specific node :math:`j` to have in-degree :math:`K_j`, are hypergeometric distributions -.. code-block:: python + .. math:: + P(K_{\text{in},j}=K_j)= + \begin{pmatrix} N_s\\K_j \end{pmatrix} \begin{pmatrix}N_sN_t-1 \\ + N_\text{syn}-K_j \end{pmatrix}\Bigg/\begin{pmatrix}N_sN_t + \\ N_\text{syn}\end{pmatrix} \,, - >>> print(nest.GetDefaults('static_synapse')) - {'delay': 1.0, - 'has_delay': True, - 'num_connections': 0, - 'receptor_type': 0, - 'requires_symmetric': False, - 'sizeof': 32, - 'synapse_model': 'static_synapse', - 'weight': 1.0, - 'weight_recorder': ()} - >>> nest.SetDefaults('static_synapse', {'weight': 2.5}) - -To further customize the process of creating synapses, it is often -useful to have the same basic synapse model available with different -parametrizations. To this end, :py:func:`.CopyModel` can be used to -create custom synapse types from already existing synapse types. In -the simplest case, it takes the names of the existing model and the -copied type to be created. The optional argument ``params`` allows to -directly customize the new type during the copy operation. If omitted, -the defaults of the copied model are taken. + with sources and targets switched for :math:`P(K_{\text{out},j}=K_j)`. -.. code-block:: python - nest.CopyModel('static_synapse', 'inhibitory', {'weight': -2.5}) - nest.Connect(A, B, syn_spec='inhibitory') -.. _inspecting_connections: +.. dropdown:: Mathematical details: Random, fixed total number with multapses -Inspecting Connections ----------------------- + | **Symbol:** :math:`\mathbf{\rho_N}(N_\text{syn})\mathbf{M}(\mathbb{N}_S \times \mathbb{N}_T)` + | **CSA:** :math:`\mathbf{\rho_N}(N_\text{syn})\mathbf{M}(\mathbb{N}_S \times \mathbb{N}_T)` + | **Definition:** :math:`N_\text{syn}\in\{0,\ldots,N_sN_t\}` edges are randomly drawn from the edge set :math:`\mathcal{E}_\mathcal{ST}` with replacement. + | If multapses are allowed, there are :math:`\begin{pmatrix}N_sN_t+N_\text{syn}-1\\N_\text{syn}\end{pmatrix}` possible networks for any given number :math:`N_\text{syn}\leq N_sN_t`. + | Because exactly :math:`N_\text{syn}` connections are distributed across :math:`N_t` targets with replacement, the joint in-degree distribution is multinomial, -In order to assert that the instantiated network model actually looks -like what was intended, it is oftentimes useful to inspect the -connections in the network. For this, NEST provides the function - -.. code-block:: python + .. math:: + \begin{equation}\label{eq:randfixKm} + \begin{split} + &P(K_{\text{in},1}=K_1,\ldots,K_{\text{in},N_t}=K_{N_t})\\ + & \quad \quad \quad=\begin{cases} + \frac{N_\text{syn}!}{K_1!...K_{N_t}!} \,p^{N_\text{syn}} & \text{if}\,\,\sum_{j=1}^{N_t} K_j = N_\text{syn}\\ + \quad\quad 0 & \text{otherwise}\end{cases}\, + \end{split} + \end{equation} + + with :math:`p=1/N_t`. - nest.GetConnections(source=None, target=None, synapse_model=None, synapse_label=None) + The out-degrees have an analogous multinomial distribution :math:`P(K_{\text{out},1}=K_1,\ldots,K_{\text{out},N_s}=K_{N_s})`, with :math:`p=1/N_s` and sources and targets switched. The marginal distributions are binomial distributions :math:`P(K_{\text{in},j}=K)= \mathcal{B}(K|N_\text{syn},1/N_t)` and :math:`P(K_{\text{out},j}=K)= \mathcal{B}(K|N_\text{syn},1/N_s)`, respectively. -This function returns a ``SynapseCollection`` object that contains the -identifiers for connections that match the given filters. ``source`` -and ``target`` are given as NodeCollections, ``synapse_model`` is the -name of the model as a string and ``synapse_label`` is an integer -identifier. Any combination of these parameters is permitted. If -``nest.GetConnections()`` is called without parameters it returns all -connections in the network. + The :math:`\mathbf{M}`-operator of CSA should not be confused with the ":math:`M`" indicating that multapses are allowed in our symbolic notation. -Internally, each connection in the SynapseCollection is represented by -the following five entries: source node ID, target node ID, thread ID -of the target, numeric synapse ID, and port. +.. _fixed_indegree: -The result of :py:func:`.GetConnections` can be further processed by -giving it as an argument to :py:func:`GetStatus`, or, better yet, by -using the :py:meth:`~.SynapseCollection.get` function on the SynapseCollection directly. Both -ways will yield a dictionary with the parameters of the connections -that match the filter criterions given to ``nest.GetConnections()``: - -.. code-block:: python - - A = nest.Create('iaf_psc_alpha', 2) - B = nest.Create('iaf_psc_alpha') - nest.Connect(A, B) - conn = nest.GetConnections() +Random, fixed indegree +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -.. code-block:: python +.. image:: ../static/img/Fixed_indegree.png + :width: 200px + :align: center - >>> conn.get() - {'delay': [1.0, 1.0], - 'port': [0, 1], - 'receptor': [0, 0], - 'sizeof': [32, 32], - 'source': [1, 2], - 'synapse_id': [18, 18], - 'synapse_model': ['static_synapse', 'static_synapse'], - 'target': [3, 3], - 'target_thread': [0, 0], - 'weight': [1.0, 1.0]} - -The ``get()`` function of a SynapseCollection can optionally also take -a string or list of strings to only retrieve specific parameters. This -is useful if you do not want to inspect the entire synapse dictionary: +The nodes in ``S`` are randomly connected with the nodes in ``T`` such +that each node in ``T`` has a fixed :hxt_ref:`indegree` of ``N``. .. code-block:: python - >>> conn.get('weight') - [1.0, 1.0] - -.. code-block:: python + n, m, N = 5, 5, 2 + S = nest.Create('iaf_psc_alpha', n) + T = nest.Create('iaf_psc_alpha', m) + conn_spec_dict = {'rule': 'fixed_indegree', 'indegree': N} + # By default multapses are allowed, + # optionally if multapses are not allowed the following parameter must be set + # conn_spec_dict = {rule': 'fixed_indegree', 'indegree': N, 'allow_autapses': False} + nest.Connect(S, T, conn_spec_dict) + +.. dropdown:: Mathematical details: Random, fixed in-degree without multapses + + | **Symbol:** :math:`K_{in}, \cancel{M}` + | **CSA:** :math:`{\rho_1}(K)(\mathbb{N}_S \times \mathbb{N}_T)` + | **Definition:** Each target node in :math:`\mathcal{T}` is connected to :math:`K_\text{in}` nodes in :math:`\mathcal{S}` randomly chosen without replacement. + | The in-degree distribution is by definition :math:`P(K)=\delta_{K,K_\text{in}}`. To obtain the out-degree distribution, observe that after one target node has drawn its :math:`K_\text{out}` sources the joint probability distribution of out-degrees :math:`K_{\text{out},j}` is multivariate-hypergeometric such that + + .. math:: + \begin{equation}\label{eq:hypmult} + \begin{split} + &P(K_{\text{out},1}=K_1, \ldots,K_{\text{out},N_s}=K_{N_s})\\ + & \quad \quad \quad= \begin{cases} + \prod_{j=1}^{N_s} \begin{pmatrix} 1\\K_j\end{pmatrix}\Bigg/\begin{pmatrix} N_s\\K_\text{in}\end{pmatrix} + & \text{if}\,\,\sum_{j=1}^{N_s} K_j = K_\text{in}\\ + \phantom{bl}0 & \text{otherwise}\end{cases}\,, \qquad (1) + \end{split} + \end{equation} + + where :math:`\forall_j\,K_j\in\{0,1\}`. + The marginal distributions are hypergeometric distributions + + .. math:: + \begin{eqnarray}\label{eq:hypmarg} + P(K_{\text{out},j}=K)= + \begin{pmatrix} 1\\K \end{pmatrix} \begin{pmatrix}N_s-1 \\ + K_\text{in}-K \end{pmatrix}\Bigg/\begin{pmatrix}N_s + \\ K_\text{in}\end{pmatrix} = \text{Ber}(K_\text{in}/N_s)\,, \qquad (2) + \end{eqnarray} + + with :math:`\text{Ber}(p)` denoting the Bernoulli distribution with parameter :math:`p`, because :math:`K\in\{0,1\}`. + The full joint distribution is the sum of :math:`N_t` independent instances of equation (1). + +.. dropdown:: Mathematical details: Random, fixed in-degree with multapses + + | **Symbol:** :math:`K_{in}, M` + | **CSA:** :math:`\mathbf{\rho_1}(K)\mathbf{M}(\mathbb{N}_S \times \mathbb{N}_T)` + | **Definition:** Each target node in :math:`\mathcal{T}` is connected to :math:`K_\text{in}` nodes in :math:`\mathcal{S}` randomly chosen with replacement. + | :math:`N_s` is the number of source nodes from which exactly :math:`K_\text{in}` connections are drawn with equal probability :math:`p=1/N_s` for each of the :math:`N_t` target nodes :math:`t_i\in\mathcal{T}`. The in-degree distribution is by definition :math:`P(K)=\delta_{K,K_\text{in}}`. To obtain the out-degree distribution, we observe that because multapses are allowed, drawing :math:`N_t` times :math:`K_{\text{in},i}=K_\text{in}` from :math:`\mathcal{S}` is equivalent to drawing :math:`N_t K_\text{in}` times with replacement from :math:`\mathcal{S}`. This procedure yields a multinomial distribution of the out-degrees :math:`K_{\text{out},j}` of source nodes :math:`s_j\in\mathcal{S}` [4]_, i.e., + + .. math:: + \begin{equation}\label{eq:rfin} + \begin{split} + &P(K_{\text{out},1}=K_1,\ldots,K_{\text{out},N_s}=K_{N_s})\\ + & \quad \quad \quad =\begin{cases} + \frac{(N_tK_\text{in})!}{K_1!...K_{N_s}!} p^{N_tK_\text{in}} & \text{if}\,\,\sum_{j=1}^{N_s} K_j = N_tK_\text{in}\\ + \quad\quad 0 & \text{otherwise}\end{cases} + \end{split} + \end{equation} + + The marginal distributions are binomial distributions + + .. math:: + \begin{equation}\label{eq:rfinmarg} + P(K_{\text{out},j}=K)= \mathcal{B}(K|N_tK_\text{in},1/N_s)\,. + \end{equation} + + + +.. _fixed_outdegree: + +Random, fixed outdegree +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - >>> conn.get(['source', 'target']) - {'source': [1, 2], 'target': [3, 3]} +.. image:: ../static/img/Fixed_outdegree.png + :width: 200px + :align: center -Another way of retrieving specific parameters is by getting them -directly from the SynapseCollection using the dot-notation: +The nodes in ``S`` are randomly connected with the nodes in ``T`` such +that each node in ``S`` has a fixed :hxt_ref:`outdegree` of ``N``. .. code-block:: python - >>> conn.delay - [1.0, 1.0] + n, m, N = 5, 5, 2 + S = nest.Create('iaf_psc_alpha', n) + T = nest.Create('iaf_psc_alpha', m) + conn_spec_dict = {'rule': 'fixed_outdegree', 'outdegree': N} + + # By default multapses are allowed, + # optionally if multapses are not allowed the following parameter must be set + # conn_spec_dict = {'rule': 'fixed_outdegree', 'outdegree': N, 'allow_autapses': False} + nest.Connect(S, T, conn_spec_dict) -For :ref:`spatially distributed networks `, you can -access the distance between the source-target pairs by querying -`distance` on your SynapseCollection. +.. dropdown:: Mathematical details: Random, fixed out-degree without multapses -.. code-block:: python + | **Symbol:** :math:`K_{out},\cancel{M}` + | **CSA:** :math:`\mathbf{\rho_0}(K)(\mathbb{N}_S \times \mathbb{N}_T)` + | **Definition:** Each source node in :math:`S` is connected to :math:`K_\text{out}` nodes in :math:`\mathcal{T}` randomly chosen without replacement. + | The out-degree distribution is by definition :math:`P(K)=\delta_{K,K_\text{out}}`, while the in-degree distribution is obtained by switching source and target indices, and replacing :math:`K_\text{out}` with :math:`K_\text{in}` in equation (2) from :ref:`Random, fixed in-degree without multapses `. - >>> spatial_conn.distance - (0.47140452079103173, - 0.33333333333333337, - 0.4714045207910317, - 0.33333333333333337, - 3.925231146709438e-17, - 0.33333333333333326, - 0.4714045207910317, - 0.33333333333333326, - 0.47140452079103157) - -You can further examine the SynapseCollection by checking the length -of it or by printing it to the terminal. The printout will be in the -form of a table that lists source and target node IDs, synapse model, -weight and delay: + +.. dropdown:: Mathematical details: Random, fixed out-degree with multapses -.. code-block:: python + | **Symbol:** :math:`K_{out}, M` + | **CSA:** :math:`\mathbf{\rho_0}(K)\mathbf{M}(\mathbb{N}_S \times \mathbb{N}_T)` + | **Definition:** Each source node in :math:`\mathcal{S}` is connected to :math:`K_\text{out}` nodes in :math:`\mathcal{T}` randomly chosen with replacement. + | By definition, the out-degree distribution is a :math:`P(K)=\delta_{K,K_\text{out}}`. The respective in-degree distribution and marginal distributions are obtained by switching source and target indices, and replacing :math:`K_\text{out}` with :math:`K_\text{in}` in equation from :ref:`Random, fixed in-degree with multapses ` [4]_. - >>> len(conn) - 2 - >>> print(conn) - source target synapse model weight delay - -------- -------- --------------- -------- ------- - 1 3 static_synapse 1.000 1.000 - 2 3 static_synapse 1.000 1.000 +.. _conn_rules: -A SynapseCollection can be indexed or sliced, if you only want to -inspect a subset of the connections contained in it: +Connection Rules +---------------- -.. code-block:: python +Connection rules are specified using the ``conn_spec`` parameter of +:py:func:`.Connect`, which can be either just a string naming a connection +rule, or a dictionary containing a rule specification. Only connection +rules requiring no parameters can be given as strings, for all other +rules, a dictionary specifying the rule and its parameters is +required. Examples for such parameters might be in- and out-degrees, +or the probability for establishing a connection. A description of +all connection rules available in NEST is available below. - >>> print(conn[0:2:2]) - source target synapse model weight delay - -------- -------- --------------- -------- ------- - 1 3 static_synapse 1.000 1.000 -Last, but not least, SynapseCollection can be iterated, to retrieve -one connection at a time: -.. code-block:: python - >>> for c in conn: - ... print(c.source) - 1 - 2 -Modifying Existing Connections ------------------------------- -To modify the parameters of an existing connection, you first have to -obtain handles to them using :py:func:`.GetConnections`. These handles -can then be given as arguments to the :py:func:`.SetStatus` function, -or by using the :py:meth:`~.SynapseCollection.set` function on the SynapseCollection directly: -.. code-block:: python +.. _conn_builder_conngen: - n1 = nest.Create('iaf_psc_alpha', 2) - n2 = nest.Create('iaf_psc_alpha', 2) - nest.Connect(n1, n2) - conn = nest.GetConnections() - conn.set(weight=2.0) +Conngen +~~~~~~~ -.. code-block:: python +.. admonition:: Availability - >>> conn.get() - {'delay': [1.0, 1.0, 1.0, 1.0], - 'port': [0, 1, 2, 3], - 'receptor': [0, 0, 0, 0], - 'sizeof': [32, 32, 32, 32], - 'source': [1, 1, 2, 2], - 'synapse_id': [18, 18, 18, 18], - 'synapse_model': ['static_synapse', 'static_synapse', 'static_synapse', static_synapse'], - 'target': [3, 4, 3, 4], - 'target_thread': [0, 0, 0, 0], - 'weight': [2.0, 2.0, 2.0, 2.0]} - -To update a single parameter of a connection or a set of connections, -you can call the ``set()`` function of the SynapseCollection with the -keyword argument ``parameter_name``. The value for this argument can -be a single value, a list, or a ``nest.Parameter``. If a single value -is given, the value is set on all connections. If you use a list to -set the parameter, the list needs to be the same length as there are -connections in the SynapseCollection. + This connection rule is only available if NEST was compiled with + :ref:`support for libneurosim `. -.. code-block:: python +To allow the generation of connectivity by means of an external +library, NEST supports the Connection Generator Interface [5]_. For +more details on this interface, see the Git repository of `libneurosim +`_. - conn.set(weight=[4.0, 4.5, 5.0, 5.5]) +In contrast to the other rules for creating connections, this rule +relies on a Connection Generator object to describe the connectivity +pattern in a library-specific way. The Connection Generator is handed +to :py:func:`.Connect` under the key ``cg`` of the connection specification +dictionary and evaluated internally. If the Connection Generator +provides values for connection weights and delays, their respective +indices can be specified under the key ``params_map``. Alternatively, +all synapse parameters can be specified using the synapse +specification argument to ``Connect()``. -Similar to how you retrieve several parameters at once with the -:py:meth:`~.SynapseCollection.get` function explained above, you can also set multiple -parameters at once using ``set(parameter_dictionary)``. Again, the -values of the dictionary can be a single value, a list, or a -``nest.Parameter``. +The following listing shows an example for using the `Connection-Set +Algebra `_ in NEST via the Connection +Generator Interface and randomly connects 10% of the neurons from +``A`` to the neurons in ``B``, each connection having a weight of +10000.0 pA and a delay of 1.0 ms: .. code-block:: python - conn.set({'weight': [1.5, 2.0, 2.5, 3.0], 'delay': 2.0}) + A = nest.Create('iaf_psc_alpha', 100) + B = nest.Create('iaf_psc_alpha', 100) -Finally, you can also directly set parameters on a SynapseCollection -using the dot-notation: + # Create the Connection Generator object + import csa + cg = csa.cset(csa.random(0.1), 10000.0, 1.0) -.. code-block:: python + # Map weight and delay indices to values from cg + params_map = {'weight': 0, 'delay': 1} - >>> conn.weight = 5. - >>> conn.weight - [5.0, 5.0, 5.0, 5.0] - >>> conn.delay = [5.1, 5.2, 5.3, 5.4] - >>> conn.delay - [5.1, 5.2, 5.3, 5.4] + conn_spec_dict = {'rule': 'conngen', 'cg': cg, 'params_map': params_map} + nest.Connect(A, B, conn_spec_dict) -Note that some parameters like ``source`` and ``target`` are read-only and -cannot be set. The documentation of a specific synapse model will -point out which parameters can be set and which are read-only. References ---------- - -.. [1] Djurfeldt M, Davison AP and Eppler JM (2014). Efficient generation of +.. [1] Djurfeldt M. The Connection-set Algebra—A Novel Formalism for the Representation of Connectivity Structure in Neuronal Network Models. Neuroinformatics. 2012; 10: 287–304. https://doi.org/10.1007/s12021-012-9146-1 +.. [2] Albert R, Barabási AL. Statistical mechanics of complex networks. Rev Mod Phys. 2002; 74: 47–97. https://doi.org/10.1103/RevModPhys.74.47 +.. [3] Erdős P, Rényi A. On random graphs. Publications Mathematicae. 1959; 6: 290–297. +.. [4] Hjertholm D. Statistical tests for connection algorithms for structured neural networks [master’s thesis]. Norwegian University of Life Sciences. Ås, Norway; 2013. Available from: http://hdl.handle.net/11250/189117 +.. [5] Djurfeldt M, Davison AP and Eppler JM (2014). Efficient generation of connectivity in neuronal networks from simulator-independent descriptions. Front. Neuroinform. https://doi.org/10.3389/fninf.2014.00043 diff --git a/doc/htmldoc/synapses/synapse_specification.rst b/doc/htmldoc/synapses/synapse_specification.rst new file mode 100644 index 0000000000..a2b9e8b695 --- /dev/null +++ b/doc/htmldoc/synapses/synapse_specification.rst @@ -0,0 +1,715 @@ +.. _synapse_spec: + +Synapse Specification +======================== + +The synapse properties can be given as just the name of the desired +synapse model as a string, as a dictionary specifying synapse model +and parameters, or as a ``CollocatedSynapse`` object creating +multiple synapses for each source-target pair as detailed in +the section on :ref:`collocated synapses `. + +.. code-block:: python + + n = 10 + A = nest.Create('iaf_psc_alpha', n) + B = nest.Create('iaf_psc_alpha', n) + nest.Connect(A, B, syn_spec='static_synapse') + + syn_spec_dict = {'synapse_model': 'stdp_synapse', + 'weight': 2.5, 'delay': 0.5} + nest.Connect(A, B, syn_spec=syn_spec_dict) + +If synapse properties are given as a dictionary, it may include the keys +``synapse_model`` (default `static_synapse`), ``weight`` (default 1.0), +``delay`` (default 1.0), and ``receptor_type`` (default 0, see :ref:`receptor-types` for details), +as well as parameters specific to the chosen synapse model. The specification of +all parameters is optional, and unspecified parameters will take on the +default values of the chosen synapse model that can be inspected using +``nest.GetDefaults(synapse_model)``. + +Parameters can be either :ref:`fixed scalar values `, +:ref:`arrays of values `, or :ref:`expressions `. + +One synapse dictionary can contain an arbitrary combination of parameter types, +as long as they are supported by the chosen connection rule. + + +.. _scalar_params: + +Scalar parameters +~~~~~~~~~~~~~~~~~ + +Scalar parameters must be given with the correct type. The ``weight`` +for instance must be a float, while the ``receptor_type`` has to be of +type integer. When a synapse parameter is given as a scalar, the value +will be applied to all connections created in the current +:py:func:`.Connect` call. + +.. code-block:: python + + n = 10 + neuron_dict = {'tau_syn': [0.3, 1.5]} + A = nest.Create('iaf_psc_exp_multisynapse', n, neuron_dict) + B = nest.Create('iaf_psc_exp_multisynapse', n, neuron_dict) + syn_spec_dict ={'synapse_model': 'static_synapse', 'weight': 2.5, 'delay': 0.5, 'receptor_type': 1} + nest.Connect(A, B, syn_spec=syn_spec_dict) + +.. _array_params: + +Array parameters +~~~~~~~~~~~~~~~~ + +Array parameters can be used with the rules ``all_to_all``, +``fixed_indegree``, ``fixed_outdegree``, ``fixed_total_number`` and +``one_to_one``. The arrays can be specified as NumPy arrays or Python +lists. As with the scalar parameters, all parameters have to be +specified as arrays of the correct type. + +rule: all-to-all +^^^^^^^^^^^^^^^^ + +When connecting with rule ``all_to_all``, the array parameter must +have dimension `len(B) x len(A)`. + +.. code-block:: python + + A = nest.Create('iaf_psc_alpha', 3) + B = nest.Create('iaf_psc_alpha', 2) + syn_spec_dict = {'weight': [[1.2, -3.5, 2.5], [0.4, -0.2, 0.7]]} + nest.Connect(A, B, syn_spec=syn_spec_dict) + +rule: fixed indegree +^^^^^^^^^^^^^^^^^^^^ + +For rule ``fixed_indegree`` the array has to be a two-dimensional +NumPy array or Python list with shape ``(len(B), indegree)``, where +:hxt_ref:`indegree` is the number of incoming connections per target neuron. +This means that the rows describe the target, while the columns +represent the connections converging on the target neuron, regardless +of the identity of the source neurons. + +.. code-block:: python + + A = nest.Create('iaf_psc_alpha', 5) + B = nest.Create('iaf_psc_alpha', 3) + conn_spec_dict = {'rule': 'fixed_indegree', 'indegree': 2} + syn_spec_dict = {'weight': [[1.2, -3.5],[0.4, -0.2],[0.6, 2.2]]} + nest.Connect(A, B, conn_spec_dict, syn_spec_dict) + +rule: fixed outdegree +^^^^^^^^^^^^^^^^^^^^^ + +For rule ``fixed_outdegree`` the array has to be a two-dimensional +NumPy array or Python list with shape ``(len(A), outdegree)``, where +:hxt_ref:`outdegree` is the number of outgoing connections per source +neuron. This means that the rows describe the source, while the +columns represent the connections starting from the source neuron +regardless of the identity of the target neuron. + +.. code-block:: python + + A = nest.Create('iaf_psc_alpha', 2) + B = nest.Create('iaf_psc_alpha', 5) + conn_spec_dict = {'rule': 'fixed_outdegree', 'outdegree': 3} + syn_spec_dict = {'weight': [[1.2, -3.5, 0.4], [-0.2, 0.6, 2.2]]} + nest.Connect(A, B, conn_spec_dict, syn_spec_dict) + +rule: fixed total number +^^^^^^^^^^^^^^^^^^^^^^^^ + +For rule ``fixed_total_number``, the array has to be same the length as the +number of connections ``N``. + +.. code-block:: python + + A = nest.Create('iaf_psc_alpha', 3) + B = nest.Create('iaf_psc_alpha', 4) + conn_spec_dict = {'rule': 'fixed_total_number', 'N': 4} + syn_spec_dict = {'weight': [1.2, -3.5, 0.4, -0.2]} + nest.Connect(A, B, conn_spec_dict, syn_spec_dict) + +rule: one-to-one +^^^^^^^^^^^^^^^^ + +For rule ``one_to_one`` the array must have the same length as there +are nodes in ``A`` and ``B``. + +.. code-block:: python + + A = nest.Create('iaf_psc_alpha', 2) + B = nest.Create('spike_recorder', 2) + conn_spec_dict = {'rule': 'one_to_one'} + syn_spec_dict = {'weight': [1.2, -3.5]} + nest.Connect(A, B, conn_spec_dict, syn_spec_dict) + +.. _dist_params: + +Expressions as parameters +~~~~~~~~~~~~~~~~~~~~~~~~~ + +``nest.Parameter`` objects support a flexible specification of synapse +parameters through expressions. This includes parameters drawn from random +distributions and +depending on spatial properties of source and target neurons. Parameters +can be combined through mathematical expressions including conditionals, +providing for a high degree of flexibility. + +The following parameters and functionalities are provided: + +- Random parameters +- Spatial parameters +- Spatially distributed parameters +- Mathematical functions +- Clipping, redrawing, and conditional parameters + +For more information, check out the guide on +:ref:`parametrization ` or the documentation on the +different :ref:`PyNEST APIs `. + +.. code-block:: python + + n = 10 + A = nest.Create('iaf_psc_alpha', n) + B = nest.Create('iaf_psc_alpha', n) + syn_spec_dict = { + 'synapse_model': 'stdp_synapse', + 'weight': 2.5, + 'delay': nest.random.uniform(min=0.8, max=2.5), + 'alpha': nest.math.redraw(nest.random.normal(mean=5.0, std=1.0), min=0.5, max=10000.) + } + nest.Connect(A, B, syn_spec=syn_spec_dict) + +In this example, the default connection rule ``all_to_all`` is used +and connections will be using synapse model :hxt_ref:`stdp_synapse`. All synapses +are created with a static weight of 2.5 and a delay that is uniformly +distributed in [0.8, 2.5]. The parameter ``alpha`` is drawn from a +normal distribution with mean 5.0 and standard deviation 1.0; +values below 0.5 and above 10000 are excluded by re-drawing if they should occur. +Thus, the actual distribution is a slightly distorted Gaussian. + +If the synapse type is supposed to have a unique name and still use +distributed parameters, it needs to be defined in two steps: + +.. code-block:: python + + n = 10 + A = nest.Create('iaf_psc_alpha', n) + B = nest.Create('iaf_psc_alpha', n) + nest.CopyModel('stdp_synapse', 'excitatory', {'weight':2.5}) + syn_dict = { + 'synapse_model': 'excitatory', + 'weight': 2.5, + 'delay': nest.random.uniform(min=0.8, max=2.5), + 'alpha': nest.math.redraw(nest.random.normal(mean=5.0, std=1.0), min=0.5, max=10000.) + } + nest.Connect(A, B, syn_spec=syn_dict) + +For further information on the available distributions see +:ref:`Random numbers in NEST `. + +.. _collocated_synapses: + +Collocated synapses +~~~~~~~~~~~~~~~~~~~ + +Some modeling applications require multiple connections between the +same pairs of nodes. An example of this could be a network, where each +pre-synaptic neuron connects with a static synapse to a modulatory +receptor on the post-synaptic neuron and with a plastic synapse to a +normal NMDA-type receptor. + +This type of connectivity is especially hard to realize when using +randomized connection rules, as the chosen pairs that are actually +connected are only known internally, and have to be retrieved manually +after the call to :py:func:`.Connect` returns. + +To ease the setup of such connectivity patterns, NEST supports a +concept called `collocated synapses`. This allows the creation of several +connections between chosen pairs of neurons (possibly with different +synapse types or parameters) in a single call to ``nest.Connect()``. + +To create collocated synapses, the synapse specification consists of +an object of type ``CollocatedSynapses``, whose constructor takes +synapse specification dictionaries as arguments and applies the given +dictionaries to each source-target pair internally. + +.. code-block:: python + + nodes = nest.Create('iaf_psc_alpha', 3) + syn_spec = nest.CollocatedSynapses({'weight': 4.0, 'delay': 1.5}, + {'synapse_model': 'stdp_synapse'}, + {'synapse_model': 'stdp_synapse', 'alpha': 3.0}) + nest.Connect(nodes, nodes, conn_spec='one_to_one', syn_spec=syn_spec) + print(nest.GetConnections().alpha) + +The example above will create 9 connections in total because there are +3 neurons times 3 synapse specifications in the :py:func:`.CollocatedSynapses` +object, and the connection rule ``one_to_one`` is used. + + +.. code-block:: python + + >>> print(nest.num_connections) + 9 + +In more detail, the connections have the following properties: + +* 3 are of type :hxt_ref:`static_synapse` with `weight` 4.0 and `delay` 1.5 +* 3 are of type :hxt_ref:`stdp_synapse` with the default value for `alpha` +* 3 are of type :hxt_ref:`stdp_synapse` with an `alpha` of 3.0. + +If you want to connect with different :ref:`receptor types +`, you can do the following: + +.. code-block:: python + + A = nest.Create('iaf_psc_exp_multisynapse', 7) + B = nest.Create('iaf_psc_exp_multisynapse', 7, {'tau_syn': [0.1 + i for i in range(7)]}) + syn_spec_dict = nest.CollocatedSynapses({'weight': 5.0, 'receptor_type': 2}, + {'weight': 1.5, 'receptor_type': 7}) + nest.Connect(A, B, 'one_to_one', syn_spec_dict) + +.. code-block:: python + + >>> print(nest.GetConnections().get()) + +You can see how many synapse parameters you have by calling ``len()`` on +your ``CollocatedSynapses`` object: + +.. code-block:: python + + >>> len(syn_spec_dict) + 2 + +Spatially-structured networks +--------------------------------- + +Nodes in NEST can be created so that they have a position in two- or +three-dimensional space. To take full advantage of the arrangement of +nodes, connection parameters can be based on the nodes' positions or +their spatial relation to each other. See :ref:`Spatially-structured +networks ` for the full information about how to create +and connect such networks. + +Connecting sparse matrices with array indexing +-------------------------------------------------- + +Oftentimes, you will find yourself in a situation, where you want to +base your connectivity on actual data instead of rules. A common +scenario is that you have a (sometimes sparse) connection matrix +coming from an experiment or from a graph algorithm. Let's assume you +have a weight matrix of the form: + +.. math:: + + W = \begin{bmatrix} + w_{11} & w_{21} & \cdots & w_{n1} \\ + w_{12} & w_{22} & \cdots & w_{n2} \\ + \vdots & \vdots & \ddots & \vdots \\ + w_{1m} & w_{2m} & \cdots & w_{nm} \\ + \end{bmatrix} + +where :math:`w_{ij}` is the weight of the connection with pre-synaptic +node :math:`i` and post-synaptic node :math:`j`. In all generality, we +can assume that some weights are zero, indicating that there is no +connection at all. + +As there is no support for creating connections from the whole matrix +directly, we will instead just iterate the pre-synaptic neurons and +connect one column at a time. We assume that there are :math:`n` +pre-synaptic nodes in the NodeCollection ``A`` and :math:`m` +post-synaptic nodes in ``B``. We also assume that we have our weight +matrix given as a two-dimensional NumPy array `W`, with :math:`n` +columns and :math:`m` rows. + +.. code-block:: python + + W = numpy.array([[0.5, 0.0, 1.5], + [1.3, 0.2, 0.0], + [0.0, 1.25, 1.3]]) + + A = nest.Create('iaf_psc_alpha', 3) + B = nest.Create('iaf_psc_alpha', 3) + + for i, pre in enumerate(A): + # Extract the weights column. + weights = W[:, i] + + # To only connect pairs with a nonzero weight, we use array indexing + # to extract the weights and post-synaptic neurons. + nonzero_indices = numpy.where(weights != 0)[0] + weights = weights[nonzero_indices] + post = B[nonzero_indices] + + # Generate an array of node IDs for the column of the weight + # matrix, with length based on the number of nonzero + # elements. The array's dtype must be an integer. + pre_array = numpy.ones(len(nonzero_indices), dtype=int) * pre.get('global_id') + + # nest.Connect() automatically converts post to a NumPy array + # because pre_array contains multiple identical node IDs. When + # also specifying a one_to_one connection rule, the arrays of + # node IDs can then be connected. + nest.Connect(pre_array, post, conn_spec='one_to_one', syn_spec={'weight': weights}) + +.. _receptor-types: + +Receptor Types +------------------- + +Conceptually, each connection in NEST terminates at a `receptor` on +the target node. The exact meaning of such a receptor depends on the +concrete type of that node. In a multi-compartment neuron, for +instance, the different compartments could be addressed as different +receptors, while another neuron model might make sets of different +synaptic parameters available for each receptor. Please refer to the +:doc:`model documentation <../models/index_neuron>` for details. + +In order to connect a pre-synaptic node to a certain receptor on a +post-synaptic node, the integer ID of the target receptor can be +supplied under the key ``receptor_type`` in the ``syn_spec`` +dictionary during the call to :py:func:`.Connect`. If unspecified, the +receptor will take on its default value of 0. If you request a +receptor that is not available in the target node, this will result in +a runtime error. + +To illustrate the concept of receptors in more detail, the following +example shows how to connect several ``iaf_psc_alpha`` neurons to the +different compartments of a multi-compartment integrate-and-fire +neuron (``iaf_cond_alpha_mc``) that are represented by different +receptors. + +.. image:: ../static/img/Receptor_types.png + :width: 200px + :align: center + +.. code-block:: python + + A1 = nest.Create('iaf_psc_alpha') + A2 = nest.Create('iaf_psc_alpha') + A3 = nest.Create('iaf_psc_alpha') + A4 = nest.Create('iaf_psc_alpha') + B = nest.Create('iaf_cond_alpha_mc') + + receptors = nest.GetDefaults('iaf_cond_alpha_mc')['receptor_types'] + +.. code-block:: python + + >>> print(receptors) + {'soma_exc': 1, + 'soma_inh': 2, + 'soma_curr': 7, + 'proximal_exc': 3 + 'proximal_inh': 4, + 'proximal_curr': 8, + 'distal_exc': 5, + 'distal_inh': 6, + 'distal_curr': 9,} + +.. code-block:: python + + nest.Connect(A1, B, syn_spec={'receptor_type': receptors['distal_inh']}) + nest.Connect(A2, B, syn_spec={'receptor_type': receptors['proximal_inh']}) + nest.Connect(A3, B, syn_spec={'receptor_type': receptors['proximal_exc']}) + nest.Connect(A4, B, syn_spec={'receptor_type': receptors['soma_inh']}) + +In the example above, we retrieve a map of available receptors and +their IDs by extracting the `receptor_types` property from the model +defaults. This functionality is, however, only available for models +with a predefined number of receptors, while models with a variable +number of receptors usually don't provide such an enumeration. + +An example for the latter are the `*_multisynapse` neuron models that +support multiple individual synaptic time constants for the different +receptors. In these models, the number of available receptors is not +predefined, but determined only by the length of the ``tau_syn`` +vector that is supplied to the model instance. The following example +shows the setup and connection of such a model in more detail: + +.. code-block:: python + + A = nest.Create('iaf_psc_alpha') + B = nest.Create('iaf_psc_exp_multisynapse', params={'tau_syn': [0.1, 0.2, 0.3]}) + + print(B.n_synapses) # This will print 3, as we set 3 different tau_syns + + nest.Connect(A, B, syn_spec={'receptor_type': 2}) + + +.. _synapse-types: + +Synapse Types +------------- + +NEST provides a number of built-in synapse models that can be used +during connection setup. The default model is the :hxt_ref:`static_synapse`, +whose only parameters ``weight`` and ``delay`` do not change over +time. Other synapse models implement learning and adaptation in the +form of long-term or short-term plasticity. A list of available +synapse models is accessible via the command ``nest.synapse_models``. +More detailed information about each of them can be found in the +:doc:`model directory <../models/index_synapse>`. + +.. note:: + Not all nodes can be connected via all available synapse types. The + events a synapse type is able to transmit is documented in the + ``Transmits`` section of the model documentation. + +All synapses store their parameters on a per-connection basis. +However, each of the built-in models is registered with the simulation +kernel in a number of different ways that slightly modify the +available properties of the connections instantiated from the model. +The different variants are indicated by specific suffixes: + +.. glossary:: + + ``_lbl`` + denotes `labeled synapses` that have an additional parameter + `synapse_label` (type: int), which can be set to a user-defined + value. In a common application this label is used to store an + additional projection identifier. Please note that using this + synapse variant may drive up the memory requirements of your + simulations significantly, as the label is stored on a + `per-synapse` basis. + + ``_hpc`` + denotes `synapses for high-performance computing scenarios`, which + have minimal memory requirements by using thread-local target node + indices internally. Use this version if you are running very large + simulations. + + ``_hom`` + denotes `homogeneous synapses` that store certain parameters like + `weight` and `delay` only once for all synapses of the same type + and can thus be used to save memory. + +The default parameter values of a synapse model can be inspected using +the command :py:func:`.GetDefaults`, which only takes the name of the +synapse model as an argument and returns a dictionary. Likewise, the +function :py:func:`.SetDefaults` takes the name of a synapse type and a +parameter dictionary as arguments and will modify the defaults of the +given model. + +.. code-block:: python + + >>> print(nest.GetDefaults('static_synapse')) + {'delay': 1.0, + 'has_delay': True, + 'num_connections': 0, + 'receptor_type': 0, + 'requires_symmetric': False, + 'sizeof': 32, + 'synapse_model': 'static_synapse', + 'weight': 1.0, + 'weight_recorder': ()} + >>> nest.SetDefaults('static_synapse', {'weight': 2.5}) + +To further customize the process of creating synapses, it is often +useful to have the same basic synapse model available with different +parametrizations. To this end, :py:func:`.CopyModel` can be used to +create custom synapse types from already existing synapse types. In +the simplest case, it takes the names of the existing model and the +copied type to be created. The optional argument ``params`` allows to +directly customize the new type during the copy operation. If omitted, +the defaults of the copied model are taken. + +.. code-block:: python + + nest.CopyModel('static_synapse', 'inhibitory', {'weight': -2.5}) + nest.Connect(A, B, syn_spec='inhibitory') + +.. _inspecting_connections: + +Inspecting Connections +---------------------- + +In order to assert that the instantiated network model actually looks +like what was intended, it is oftentimes useful to inspect the +connections in the network. For this, NEST provides the function + +.. code-block:: python + + nest.GetConnections(source=None, target=None, synapse_model=None, synapse_label=None) + +This function returns a ``SynapseCollection`` object that contains the +identifiers for connections that match the given filters. ``source`` +and ``target`` are given as NodeCollections, ``synapse_model`` is the +name of the model as a string and ``synapse_label`` is an integer +identifier. Any combination of these parameters is permitted. If +``nest.GetConnections()`` is called without parameters it returns all +connections in the network. + +Internally, each connection in the SynapseCollection is represented by +the following five entries: source node ID, target node ID, thread ID +of the target, numeric synapse ID, and port. + +The result of :py:func:`.GetConnections` can be further processed by +giving it as an argument to :py:func:`GetStatus`, or, better yet, by +using the :py:meth:`~.SynapseCollection.get` function on the SynapseCollection directly. Both +ways will yield a dictionary with the parameters of the connections +that match the filter criterions given to ``nest.GetConnections()``: + +.. code-block:: python + + A = nest.Create('iaf_psc_alpha', 2) + B = nest.Create('iaf_psc_alpha') + nest.Connect(A, B) + conn = nest.GetConnections() + +.. code-block:: python + + >>> conn.get() + {'delay': [1.0, 1.0], + 'port': [0, 1], + 'receptor': [0, 0], + 'sizeof': [32, 32], + 'source': [1, 2], + 'synapse_id': [18, 18], + 'synapse_model': ['static_synapse', 'static_synapse'], + 'target': [3, 3], + 'target_thread': [0, 0], + 'weight': [1.0, 1.0]} + +The ``get()`` function of a SynapseCollection can optionally also take +a string or list of strings to only retrieve specific parameters. This +is useful if you do not want to inspect the entire synapse dictionary: + +.. code-block:: python + + >>> conn.get('weight') + [1.0, 1.0] + +.. code-block:: python + + >>> conn.get(['source', 'target']) + {'source': [1, 2], 'target': [3, 3]} + +Another way of retrieving specific parameters is by getting them +directly from the SynapseCollection using the dot-notation: + +.. code-block:: python + + >>> conn.delay + [1.0, 1.0] + +For :ref:`spatially distributed networks `, you can +access the distance between the source-target pairs by querying +`distance` on your SynapseCollection. + +.. code-block:: python + + >>> spatial_conn.distance + (0.47140452079103173, + 0.33333333333333337, + 0.4714045207910317, + 0.33333333333333337, + 3.925231146709438e-17, + 0.33333333333333326, + 0.4714045207910317, + 0.33333333333333326, + 0.47140452079103157) + +You can further examine the SynapseCollection by checking the length +of it or by printing it to the terminal. The printout will be in the +form of a table that lists source and target node IDs, synapse model, +weight and delay: + +.. code-block:: python + + >>> len(conn) + 2 + >>> print(conn) + source target synapse model weight delay + -------- -------- --------------- -------- ------- + 1 3 static_synapse 1.000 1.000 + 2 3 static_synapse 1.000 1.000 + +A SynapseCollection can be indexed or sliced, if you only want to +inspect a subset of the connections contained in it: + +.. code-block:: python + + >>> print(conn[0:2:2]) + source target synapse model weight delay + -------- -------- --------------- -------- ------- + 1 3 static_synapse 1.000 1.000 + +Last, but not least, SynapseCollection can be iterated, to retrieve +one connection at a time: + +.. code-block:: python + + >>> for c in conn: + ... print(c.source) + 1 + 2 + +Modifying Existing Connections +------------------------------ + +To modify the parameters of an existing connection, you first have to +obtain handles to them using :py:func:`.GetConnections`. These handles +can then be given as arguments to the :py:func:`.SetStatus` function, +or by using the :py:meth:`~.SynapseCollection.set` function on the SynapseCollection directly: + +.. code-block:: python + + n1 = nest.Create('iaf_psc_alpha', 2) + n2 = nest.Create('iaf_psc_alpha', 2) + nest.Connect(n1, n2) + conn = nest.GetConnections() + conn.set(weight=2.0) + +.. code-block:: python + + >>> conn.get() + {'delay': [1.0, 1.0, 1.0, 1.0], + 'port': [0, 1, 2, 3], + 'receptor': [0, 0, 0, 0], + 'sizeof': [32, 32, 32, 32], + 'source': [1, 1, 2, 2], + 'synapse_id': [18, 18, 18, 18], + 'synapse_model': ['static_synapse', 'static_synapse', 'static_synapse', static_synapse'], + 'target': [3, 4, 3, 4], + 'target_thread': [0, 0, 0, 0], + 'weight': [2.0, 2.0, 2.0, 2.0]} + +To update a single parameter of a connection or a set of connections, +you can call the ``set()`` function of the SynapseCollection with the +keyword argument ``parameter_name``. The value for this argument can +be a single value, a list, or a ``nest.Parameter``. If a single value +is given, the value is set on all connections. If you use a list to +set the parameter, the list needs to be the same length as there are +connections in the SynapseCollection. + +.. code-block:: python + + conn.set(weight=[4.0, 4.5, 5.0, 5.5]) + +Similar to how you retrieve several parameters at once with the +:py:meth:`~.SynapseCollection.get` function explained above, you can also set multiple +parameters at once using ``set(parameter_dictionary)``. Again, the +values of the dictionary can be a single value, a list, or a +``nest.Parameter``. + +.. code-block:: python + + conn.set({'weight': [1.5, 2.0, 2.5, 3.0], 'delay': 2.0}) + +Finally, you can also directly set parameters on a SynapseCollection +using the dot-notation: + +.. code-block:: python + + >>> conn.weight = 5. + >>> conn.weight + [5.0, 5.0, 5.0, 5.0] + >>> conn.delay = [5.1, 5.2, 5.3, 5.4] + >>> conn.delay + [5.1, 5.2, 5.3, 5.4] + +Note that some parameters like ``source`` and ``target`` are read-only and +cannot be set. The documentation of a specific synapse model will +point out which parameters can be set and which are read-only. + diff --git a/doc/htmldoc/templates/page.html b/doc/htmldoc/templates/page.html new file mode 100644 index 0000000000..b8163bddeb --- /dev/null +++ b/doc/htmldoc/templates/page.html @@ -0,0 +1,14 @@ +{% extends "!page.html" %} + +{% block footer %} + +{% endblock %} From f6af9eb49eba330b35b61a36ef70fcbdba2d6537 Mon Sep 17 00:00:00 2001 From: clemensk0 Date: Tue, 19 Sep 2023 12:45:24 +0200 Subject: [PATCH 02/37] connection management documentation --- .../synapses/connection_management.rst | 26 ++++++++++--------- 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/doc/htmldoc/synapses/connection_management.rst b/doc/htmldoc/synapses/connection_management.rst index cb46a07cb4..bc495f973e 100644 --- a/doc/htmldoc/synapses/connection_management.rst +++ b/doc/htmldoc/synapses/connection_management.rst @@ -59,9 +59,9 @@ Connection Management -We here provide formal definitions of connectivity concepts for neuronal network models. These concepts encompass the basic connectivity rules illustrated above which are already commonly used by the computational neuroscience community. Beyond that, we discuss concepts to reflect some of the richness of anatomical brain connectivity and complement in particular non-spatial connectivity rules with rules for spatially organized connectivity. +We here provide formal definitions of connectivity concepts for neuronal network models. These concepts encompass the basic connectivity rules illustrated above which are already commonly used by the computational neuroscience community. Beyond that, we discuss concepts to reflect some of the richness of anatomical brain connectivity and complement in particular non-spatial connectivity rules with rules for spatially organized connectivity. Much of the information on this page is based on the paper "Connectivity concepts in neuronal network modeling" [1]_. -For each high-level connectivity rule, we give both an algorithmic construction rule and the resulting connectivity distribution. Modelers can use these definitions to succinctly specify connection rules in their studies. However, if details differ from our standard definitions, these details should still be specified. Furthermore, we suggest symbols that can be used to indicate the corresponding connectivity types in network diagrams and add the corresponding CSA expressions from [1]_. +For each high-level connectivity rule, we give both an algorithmic construction rule and the resulting connectivity distribution. Modelers can use these definitions to succinctly specify connection rules in their studies. However, if details differ from our standard definitions, these details should still be specified. Furthermore, we suggest symbols that can be used to indicate the corresponding connectivity types in network diagrams and add the corresponding CSA expressions from [2]_. In the specification of connectivity concepts we use the following notations and definitions. Let :math:`\mathcal{S}=\{s_1,\ldots, s_{N_s}\}` be the ordered set of sources of cardinality :math:`N_s` and :math:`\mathcal{T}=\{t_1,\ldots, t_{N_t}\}` the set of targets of cardinality :math:`N_t`. Then the set of all possible directed edges between members of :math:`\mathcal{S}` and :math:`\mathcal{T}` is given by the Cartesian product :math:`\mathcal{E}_{ST}=\mathcal{S \times T}` of cardinality :math:`N_s\cdot N_t`. @@ -253,7 +253,7 @@ created with probability ``p``. | **Symbol:** :math:`p` | **CSA:** :math:`\rho(p)` | **Definition:** Each pair of nodes, with source in :math:`\mathcal{S}` and target in :math:`\mathcal{T}`, is connected with probability :math:`p`. - | In its standard form this rule cannot produce multapses since each possible edge is visited only once. If :math:`\mathcal{S=T}`, this concept is similar to Erdős-Rényi-graphs of the *constant probability* :math:`p-ensemble\ G(N,p)`---also called *binomial ensemble* [2]_; the only difference being that we here consider directed graphs, whereas the Erdős-Rényi model is undirected. The distribution of both in- and out-degrees is binomial, + | In its standard form this rule cannot produce multapses since each possible edge is visited only once. If :math:`\mathcal{S=T}`, this concept is similar to Erdős-Rényi-graphs of the *constant probability* :math:`p-ensemble\ G(N,p)`---also called *binomial ensemble* [3]_; the only difference being that we here consider directed graphs, whereas the Erdős-Rényi model is undirected. The distribution of both in- and out-degrees is binomial, .. math:: P(K_\text{in}=K)=\mathcal{B}(K|N_s,p):=\begin{pmatrix}N_s\\K\end{pmatrix}p^{K}(1-p)^{N_s-K} @@ -315,7 +315,7 @@ such that the total number of connections equals ``N``. | **Symbol:** :math:`N_\text{syn} \cancel{M}` | **CSA:** :math:`\mathbf{\rho_{N}}(N_\text{syn})(\mathbb{N}_S \times \mathbb{N}_T)` | **Definition:** :math:`N_\text{syn}\in\{0,\ldots,N_sN_t\}` edges are randomly drawn from the edge set :math:`\mathcal{E}_\mathcal{ST}` without replacement. - | For :math:`\mathcal{S}=\mathcal{T}` this is a directed graph generalization of Erdős-Rényi graphs of the *constant number of edges* :math:`N_\text{syn}`-ensemble :math:`G(N,N_\text{syn})` [3]_. There are :math:`\begin{pmatrix}N_s N_t\\N_\text{syn}\end{pmatrix}` possible networks for any given number :math:`N_\text{syn}\leq N_sN_t`, which all have the same probability. The resulting in- and out-degree distributions are multivariate hypergeometric distributions. + | For :math:`\mathcal{S}=\mathcal{T}` this is a directed graph generalization of Erdős-Rényi graphs of the *constant number of edges* :math:`N_\text{syn}`-ensemble :math:`G(N,N_\text{syn})` [4]_. There are :math:`\begin{pmatrix}N_s N_t\\N_\text{syn}\end{pmatrix}` possible networks for any given number :math:`N_\text{syn}\leq N_sN_t`, which all have the same probability. The resulting in- and out-degree distributions are multivariate hypergeometric distributions. .. math:: \begin{split} @@ -425,7 +425,7 @@ that each node in ``T`` has a fixed :hxt_ref:`indegree` of ``N``. | **Symbol:** :math:`K_{in}, M` | **CSA:** :math:`\mathbf{\rho_1}(K)\mathbf{M}(\mathbb{N}_S \times \mathbb{N}_T)` | **Definition:** Each target node in :math:`\mathcal{T}` is connected to :math:`K_\text{in}` nodes in :math:`\mathcal{S}` randomly chosen with replacement. - | :math:`N_s` is the number of source nodes from which exactly :math:`K_\text{in}` connections are drawn with equal probability :math:`p=1/N_s` for each of the :math:`N_t` target nodes :math:`t_i\in\mathcal{T}`. The in-degree distribution is by definition :math:`P(K)=\delta_{K,K_\text{in}}`. To obtain the out-degree distribution, we observe that because multapses are allowed, drawing :math:`N_t` times :math:`K_{\text{in},i}=K_\text{in}` from :math:`\mathcal{S}` is equivalent to drawing :math:`N_t K_\text{in}` times with replacement from :math:`\mathcal{S}`. This procedure yields a multinomial distribution of the out-degrees :math:`K_{\text{out},j}` of source nodes :math:`s_j\in\mathcal{S}` [4]_, i.e., + | :math:`N_s` is the number of source nodes from which exactly :math:`K_\text{in}` connections are drawn with equal probability :math:`p=1/N_s` for each of the :math:`N_t` target nodes :math:`t_i\in\mathcal{T}`. The in-degree distribution is by definition :math:`P(K)=\delta_{K,K_\text{in}}`. To obtain the out-degree distribution, we observe that because multapses are allowed, drawing :math:`N_t` times :math:`K_{\text{in},i}=K_\text{in}` from :math:`\mathcal{S}` is equivalent to drawing :math:`N_t K_\text{in}` times with replacement from :math:`\mathcal{S}`. This procedure yields a multinomial distribution of the out-degrees :math:`K_{\text{out},j}` of source nodes :math:`s_j\in\mathcal{S}` [5]_, i.e., .. math:: \begin{equation}\label{eq:rfin} @@ -483,7 +483,7 @@ that each node in ``S`` has a fixed :hxt_ref:`outdegree` of ``N``. | **Symbol:** :math:`K_{out}, M` | **CSA:** :math:`\mathbf{\rho_0}(K)\mathbf{M}(\mathbb{N}_S \times \mathbb{N}_T)` | **Definition:** Each source node in :math:`\mathcal{S}` is connected to :math:`K_\text{out}` nodes in :math:`\mathcal{T}` randomly chosen with replacement. - | By definition, the out-degree distribution is a :math:`P(K)=\delta_{K,K_\text{out}}`. The respective in-degree distribution and marginal distributions are obtained by switching source and target indices, and replacing :math:`K_\text{out}` with :math:`K_\text{in}` in equation from :ref:`Random, fixed in-degree with multapses ` [4]_. + | By definition, the out-degree distribution is a :math:`P(K)=\delta_{K,K_\text{out}}`. The respective in-degree distribution and marginal distributions are obtained by switching source and target indices, and replacing :math:`K_\text{out}` with :math:`K_\text{in}` in equation from :ref:`Random, fixed in-degree with multapses ` [5]_. .. _conn_rules: @@ -516,7 +516,7 @@ Conngen :ref:`support for libneurosim `. To allow the generation of connectivity by means of an external -library, NEST supports the Connection Generator Interface [5]_. For +library, NEST supports the Connection Generator Interface [6]_. For more details on this interface, see the Git repository of `libneurosim `_. @@ -554,11 +554,13 @@ Generator Interface and randomly connects 10% of the neurons from References ---------- -.. [1] Djurfeldt M. The Connection-set Algebra—A Novel Formalism for the Representation of Connectivity Structure in Neuronal Network Models. Neuroinformatics. 2012; 10: 287–304. https://doi.org/10.1007/s12021-012-9146-1 -.. [2] Albert R, Barabási AL. Statistical mechanics of complex networks. Rev Mod Phys. 2002; 74: 47–97. https://doi.org/10.1103/RevModPhys.74.47 -.. [3] Erdős P, Rényi A. On random graphs. Publications Mathematicae. 1959; 6: 290–297. -.. [4] Hjertholm D. Statistical tests for connection algorithms for structured neural networks [master’s thesis]. Norwegian University of Life Sciences. Ås, Norway; 2013. Available from: http://hdl.handle.net/11250/189117 -.. [5] Djurfeldt M, Davison AP and Eppler JM (2014). Efficient generation of +.. [1] Senk J, Kriener B, Djurfeldt M, Voges N, Jiang HJ, et al. (2022) Connectivity concepts in neuronal network modeling. PLOS Computational Biology 18(9): e1010086. https://doi.org/10.1371/journal.pcbi.1010086 +.. [2] Djurfeldt M. The Connection-set Algebra—A Novel Formalism for the Representation of Connectivity Structure in Neuronal Network Models. Neuroinformatics. 2012; 10: 287–304. https://doi.org/10.1007/s12021-012-9146-1 +.. [3] Albert R, Barabási AL. Statistical mechanics of complex networks. Rev Mod Phys. 2002; 74: 47–97. https://doi.org/10.1103/RevModPhys.74.47 +.. [4] Erdős P, Rényi A. On random graphs. Publications Mathematicae. 1959; 6: 290–297. +.. [5] Hjertholm D. Statistical tests for connection algorithms for structured neural networks [master’s thesis]. Norwegian University of Life Sciences. Ås, Norway; 2013. Available from: http://hdl.handle.net/11250/189117 +.. [6] Djurfeldt M, Davison AP and Eppler JM (2014). Efficient generation of connectivity in neuronal networks from simulator-independent descriptions. Front. Neuroinform. https://doi.org/10.3389/fninf.2014.00043 + From 3c273c44ba5640f8b13f24809043f198571440eb Mon Sep 17 00:00:00 2001 From: Johanna Senk Date: Fri, 20 Oct 2023 11:53:49 +0200 Subject: [PATCH 03/37] Move introduction of Connect() function to top --- .../synapses/connection_management.rst | 25 ++++++++----------- 1 file changed, 11 insertions(+), 14 deletions(-) diff --git a/doc/htmldoc/synapses/connection_management.rst b/doc/htmldoc/synapses/connection_management.rst index bc495f973e..40a9afb3ea 100644 --- a/doc/htmldoc/synapses/connection_management.rst +++ b/doc/htmldoc/synapses/connection_management.rst @@ -56,20 +56,6 @@ Connection Management :link-type: ref .. image:: ../static/img/Multapse_H.png - - - -We here provide formal definitions of connectivity concepts for neuronal network models. These concepts encompass the basic connectivity rules illustrated above which are already commonly used by the computational neuroscience community. Beyond that, we discuss concepts to reflect some of the richness of anatomical brain connectivity and complement in particular non-spatial connectivity rules with rules for spatially organized connectivity. Much of the information on this page is based on the paper "Connectivity concepts in neuronal network modeling" [1]_. - -For each high-level connectivity rule, we give both an algorithmic construction rule and the resulting connectivity distribution. Modelers can use these definitions to succinctly specify connection rules in their studies. However, if details differ from our standard definitions, these details should still be specified. Furthermore, we suggest symbols that can be used to indicate the corresponding connectivity types in network diagrams and add the corresponding CSA expressions from [2]_. - -In the specification of connectivity concepts we use the following notations and definitions. Let :math:`\mathcal{S}=\{s_1,\ldots, s_{N_s}\}` be the ordered set of sources of cardinality :math:`N_s` and :math:`\mathcal{T}=\{t_1,\ldots, t_{N_t}\}` the set of targets of cardinality :math:`N_t`. Then the set of all possible directed edges between members of :math:`\mathcal{S}` and :math:`\mathcal{T}` is given by the Cartesian product :math:`\mathcal{E}_{ST}=\mathcal{S \times T}` of cardinality :math:`N_s\cdot N_t`. - -If the source and target populations are identical (:math:`\mathcal{S= T}`) a source can be its own target. We call such a self-connection an :ref:`autapse `. -If autapses are not allowed, the target set for any node :math:`i \in \mathcal{S}` is :math:`\mathcal{T=S} \setminus i`, with cardinality :math:`N_t=N_s-1`. -If there is more than one edge between a source and target (or from a node to itself), we call this a :ref:`multapse `. - -The :math:`{degree\ distribution}\ P(k)` is the distribution across nodes of the number of edges per node. In a directed network, the distribution of the number of edges going out of (into) a node is called the :math:`{out\!-\!degree} (in\!-\!degree)` distribution. The distributions given below describe the effect of applying a connection rule once to a given :math:`\mathcal{S}-\mathcal{T}` pair. Connections between populations of neurons and between neurons and devices for stimulation and recording in NEST are created with the @@ -131,6 +117,17 @@ Have a look at the :ref:`inspecting_connections` section further down to get more tips on how to examine the connections in greater detail. +We here provide formal definitions of connectivity concepts for neuronal network models. These concepts encompass the basic connectivity rules illustrated above which are already commonly used by the computational neuroscience community. Beyond that, we discuss concepts to reflect some of the richness of anatomical brain connectivity and complement in particular non-spatial connectivity rules with rules for spatially organized connectivity. Much of the information on this page is based on the paper "Connectivity concepts in neuronal network modeling" [1]_. + +For each high-level connectivity rule, we give both an algorithmic construction rule and the resulting connectivity distribution. Modelers can use these definitions to succinctly specify connection rules in their studies. However, if details differ from our standard definitions, these details should still be specified. Furthermore, we suggest symbols that can be used to indicate the corresponding connectivity types in network diagrams and add the corresponding CSA expressions from [2]_. + +In the specification of connectivity concepts we use the following notations and definitions. Let :math:`\mathcal{S}=\{s_1,\ldots, s_{N_s}\}` be the ordered set of sources of cardinality :math:`N_s` and :math:`\mathcal{T}=\{t_1,\ldots, t_{N_t}\}` the set of targets of cardinality :math:`N_t`. Then the set of all possible directed edges between members of :math:`\mathcal{S}` and :math:`\mathcal{T}` is given by the Cartesian product :math:`\mathcal{E}_{ST}=\mathcal{S \times T}` of cardinality :math:`N_s\cdot N_t`. + +If the source and target populations are identical (:math:`\mathcal{S= T}`) a source can be its own target. We call such a self-connection an :ref:`autapse `. +If autapses are not allowed, the target set for any node :math:`i \in \mathcal{S}` is :math:`\mathcal{T=S} \setminus i`, with cardinality :math:`N_t=N_s-1`. +If there is more than one edge between a source and target (or from a node to itself), we call this a :ref:`multapse `. + +The :math:`{degree\ distribution}\ P(k)` is the distribution across nodes of the number of edges per node. In a directed network, the distribution of the number of edges going out of (into) a node is called the :math:`{out\!-\!degree} (in\!-\!degree)` distribution. The distributions given below describe the effect of applying a connection rule once to a given :math:`\mathcal{S}-\mathcal{T}` pair. .. _multapse_autapse: From 8bdb6918c1756db91606b5500f1893d3266ea4fc Mon Sep 17 00:00:00 2001 From: Johanna Senk Date: Mon, 13 Nov 2023 09:52:20 +0100 Subject: [PATCH 04/37] Start editing connection management --- .../synapses/connection_management.rst | 86 +++++++------------ 1 file changed, 32 insertions(+), 54 deletions(-) diff --git a/doc/htmldoc/synapses/connection_management.rst b/doc/htmldoc/synapses/connection_management.rst index 40a9afb3ea..3e07dff7c5 100644 --- a/doc/htmldoc/synapses/connection_management.rst +++ b/doc/htmldoc/synapses/connection_management.rst @@ -57,53 +57,36 @@ Connection Management .. image:: ../static/img/Multapse_H.png -Connections between populations of neurons and between neurons and -devices for stimulation and recording in NEST are created with the -:py:func:`.Connect` function. Although each connection is internally -represented individually, you can use a single call to ``Connect()`` -to create many connections at the same time. In the simplest case, the -function just takes two NodeCollections containing the source and -target nodes that will be connected in an all-to-all fashion. - -Each call to the function will establish the connectivity between pre- -and post-synaptic populations according to a certain pattern or -rule. The desired pattern is specified by simply stating the rule name -as third argument to ``Connect()``, or by setting the key ``rule`` in -the connectivity specification dictionary ``conn_spec`` alongside -additional rule-specific parameters. All available patterns are -described in the section :ref:`Connection Rules ` below. - -To specify the properties for the individual connections, a synapse -specification ``syn_spec`` can be given to ``Connect()``. This can -also just be the name of the synapse model to be used, an object -defining :ref:`collocated synapses `, or a -dictionary, with optional parameters for the connections. - -The ``syn_spec`` is given as the fourth argument to the ``Connect()`` -function. Parameters like the synaptic weight or delay can be either -set values or drawn and combined flexibly from random distributions. -More information about synapse models and their parameters can -be found in the section :ref:`Synapse Specification `. - -The :py:func:`.Connect` function can be called in any of the following ways: +We use the term `connection` to mean a single, atomic edge between network nodes (i.e., neurons or devices). +A `projection` is a group of edges that connects groups of nodes with similar properties (i.e., populations). +We specify network connectivity by providing, for each projection between any pair of populations, a *connection rule* which defines how to create atomic edges (connections) between individual nodes. +A projection is thus defined by a triplet of source population, target population and `connection rule` and represents a collection of atomic connections. + +This documentation not only describes how to define projections in NEST, but also represents a living reference for the connection rules defined in the article "Connectivity concepts in neuronal network modeling" [1]_. +The same article also introduces a graphical notation for neuronal network diagrams which are curated in the documentation of NEST Desktop. TODO ADD LINK + +Projections are created with the :py:func:`.Connect` function: .. code-block:: python - Connect(pre, post) - Connect(pre, post, conn_spec) - Connect(pre, post, conn_spec, syn_spec) + nest.Connect(pre, post) + nest.Connect(pre, post, conn_spec) + nest.Connect(pre, post, conn_spec, syn_spec) + +In the simplest case, the function just takes the ``NodeCollections`` ``pre`` and ``post``, defining the nodes of +origin (`sources`) and termination (`targets`) for the connections to be established with the default rule ``all-to-all`` and the synapse model :hxt_ref:`static_synapse`. -``pre`` and ``post`` are ``NodeCollections``, defining the nodes of -origin (`sources`) and termination (`targets`) for the connections to -be established. +Other connectivity patterns can be achieved by passing a connectivity specification dictionary ``conn_spec`` specifying a ``rule`` alongside additional rule-specific parameters. +Rules that do not require parameters can be directly provided as string instead of the dictionary (for example, ``nest.Connect(pre, post, 'one_to_one')``). +Examples of parameters might be in- and out-degrees, or the probability for establishing a connection. +All available rules are described in the section :ref:`Connection Rules ` below. -If ``conn_spec`` is not specified, the default connection rule -``all_to_all`` will be used. +Properties of individual connections (i.e., synapses) can be set via the synapse specification dictionary ``syn_spec``. +Parameters like the synaptic weight or delay can be either set values or drawn and combined flexibly from random distributions. +It is also possible to define multiple projections with different synapse properties in the same :py:func:`.Connect` call (see :ref:`collocated synapses `). +For details on synapse models and their parameters refer to :ref:`Synapse Specification `. -The synapse specification ``syn_spec`` defaults to the synapse model -:hxt_ref:`static_synapse`. By using the keyword variant (``Connect(pre, post, -syn_spec=syn_spec_dict)``), ``conn_spec`` can be omitted in the call -to :py:func:`.Connect` and will just take on the default value. +By using the keyword variant (``nest.Connect(pre, post, syn_spec=syn_spec_dict)``), ``conn_spec`` can be omitted in the call to :py:func:`.Connect` and will just take on the default value. After your connections are established, a quick sanity check is to look up the number of connections in the network, which can be easily @@ -113,9 +96,12 @@ done using the corresponding kernel attribute: print(nest.num_connections) -Have a look at the :ref:`inspecting_connections` section further down -to get more tips on how to examine the connections in greater detail. +Have a look at the section :ref:`inspecting_connections` to get more tips on how to examine the connections in greater detail. +.. _conn_rules: + +Connection Rules +---------------- We here provide formal definitions of connectivity concepts for neuronal network models. These concepts encompass the basic connectivity rules illustrated above which are already commonly used by the computational neuroscience community. Beyond that, we discuss concepts to reflect some of the richness of anatomical brain connectivity and complement in particular non-spatial connectivity rules with rules for spatially organized connectivity. Much of the information on this page is based on the paper "Connectivity concepts in neuronal network modeling" [1]_. @@ -130,6 +116,9 @@ If there is more than one edge between a source and target (or from a node to it The :math:`{degree\ distribution}\ P(k)` is the distribution across nodes of the number of edges per node. In a directed network, the distribution of the number of edges going out of (into) a node is called the :math:`{out\!-\!degree} (in\!-\!degree)` distribution. The distributions given below describe the effect of applying a connection rule once to a given :math:`\mathcal{S}-\mathcal{T}` pair. +TODO EXPLAIN DEVIATIONS FROM CC + + .. _multapse_autapse: Autapses and multapses @@ -482,19 +471,8 @@ that each node in ``S`` has a fixed :hxt_ref:`outdegree` of ``N``. | **Definition:** Each source node in :math:`\mathcal{S}` is connected to :math:`K_\text{out}` nodes in :math:`\mathcal{T}` randomly chosen with replacement. | By definition, the out-degree distribution is a :math:`P(K)=\delta_{K,K_\text{out}}`. The respective in-degree distribution and marginal distributions are obtained by switching source and target indices, and replacing :math:`K_\text{out}` with :math:`K_\text{in}` in equation from :ref:`Random, fixed in-degree with multapses ` [5]_. -.. _conn_rules: -Connection Rules ----------------- -Connection rules are specified using the ``conn_spec`` parameter of -:py:func:`.Connect`, which can be either just a string naming a connection -rule, or a dictionary containing a rule specification. Only connection -rules requiring no parameters can be given as strings, for all other -rules, a dictionary specifying the rule and its parameters is -required. Examples for such parameters might be in- and out-degrees, -or the probability for establishing a connection. A description of -all connection rules available in NEST is available below. From a3adb6917cda411913ba73185cc529eec829adcd Mon Sep 17 00:00:00 2001 From: Johanna Senk Date: Thu, 16 Nov 2023 13:22:23 +0100 Subject: [PATCH 05/37] Further revise connection management --- .../synapses/connection_management.rst | 293 +++++++++--------- 1 file changed, 146 insertions(+), 147 deletions(-) diff --git a/doc/htmldoc/synapses/connection_management.rst b/doc/htmldoc/synapses/connection_management.rst index 3e07dff7c5..0a6b9cfedf 100644 --- a/doc/htmldoc/synapses/connection_management.rst +++ b/doc/htmldoc/synapses/connection_management.rst @@ -1,6 +1,6 @@ .. _connection_management: -Connection Management +Connection management ===================== .. grid:: 4 @@ -46,26 +46,29 @@ Connection Management .. image:: ../static/img/Pairwise_bernoulli_H.png .. card:: - :link: multapse_autapse + :link: autapse_multapse :link-type: ref .. image:: ../static/img/Autapse_H.png .. card:: - :link: multapse_autapse + :link: autapse_multapse :link-type: ref .. image:: ../static/img/Multapse_H.png +Basic connection rules commonly used in the computational neuroscience community. +For more details, go to the section :ref:`Connection rules ` or just click on one of the illustrations. + +This documentation not only describes how to define network connectivity in NEST, but also represents a living reference for the connection rules defined in the article "Connectivity concepts in neuronal network modeling" [1]_. +The same article also introduces a graphical notation for neuronal network diagrams which is curated in the documentation of NEST Desktop. TODO ADD LINK +For spatially structured networks, refer to TODO. + We use the term `connection` to mean a single, atomic edge between network nodes (i.e., neurons or devices). A `projection` is a group of edges that connects groups of nodes with similar properties (i.e., populations). -We specify network connectivity by providing, for each projection between any pair of populations, a *connection rule* which defines how to create atomic edges (connections) between individual nodes. -A projection is thus defined by a triplet of source population, target population and `connection rule` and represents a collection of atomic connections. - -This documentation not only describes how to define projections in NEST, but also represents a living reference for the connection rules defined in the article "Connectivity concepts in neuronal network modeling" [1]_. -The same article also introduces a graphical notation for neuronal network diagrams which are curated in the documentation of NEST Desktop. TODO ADD LINK +To specify network connectivity, each projection is specified by a triplet of source population, target population, and a `connection rule` which defines how to connect the individual nodes. -Projections are created with the :py:func:`.Connect` function: +Projections are created in NEST with the :py:func:`.Connect` function: .. code-block:: python @@ -76,17 +79,16 @@ Projections are created with the :py:func:`.Connect` function: In the simplest case, the function just takes the ``NodeCollections`` ``pre`` and ``post``, defining the nodes of origin (`sources`) and termination (`targets`) for the connections to be established with the default rule ``all-to-all`` and the synapse model :hxt_ref:`static_synapse`. -Other connectivity patterns can be achieved by passing a connectivity specification dictionary ``conn_spec`` specifying a ``rule`` alongside additional rule-specific parameters. -Rules that do not require parameters can be directly provided as string instead of the dictionary (for example, ``nest.Connect(pre, post, 'one_to_one')``). +Other connectivity patterns can be achieved by explicitly specifying the connection rule with the connectivity specification dictionary ``conn_spec`` which expects a ``rule`` alongside additional rule-specific parameters. +Rules that do not require parameters can be directly provided as string instead of the dictionary; for example, ``nest.Connect(pre, post, 'one_to_one')``. Examples of parameters might be in- and out-degrees, or the probability for establishing a connection. -All available rules are described in the section :ref:`Connection Rules ` below. +All available rules are described in the section :ref:`Connection rules ` below. Properties of individual connections (i.e., synapses) can be set via the synapse specification dictionary ``syn_spec``. Parameters like the synaptic weight or delay can be either set values or drawn and combined flexibly from random distributions. -It is also possible to define multiple projections with different synapse properties in the same :py:func:`.Connect` call (see :ref:`collocated synapses `). -For details on synapse models and their parameters refer to :ref:`Synapse Specification `. +For details on synapse models and their parameters refer to :ref:`Synapse specification `.Note that is also possible to define multiple projections with different synapse properties in the same :py:func:`.Connect` call (see :ref:`collocated synapses `). -By using the keyword variant (``nest.Connect(pre, post, syn_spec=syn_spec_dict)``), ``conn_spec`` can be omitted in the call to :py:func:`.Connect` and will just take on the default value. +By using the keyword variant ``nest.Connect(pre, post, syn_spec=syn_spec)``, the `conn_spec` can be omitted in the call to :py:func:`.Connect` and will just take on the default value ``all-to-all``. After your connections are established, a quick sanity check is to look up the number of connections in the network, which can be easily @@ -96,30 +98,27 @@ done using the corresponding kernel attribute: print(nest.num_connections) -Have a look at the section :ref:`inspecting_connections` to get more tips on how to examine the connections in greater detail. +Have a look at the section :ref:`inspecting_connections` to get more tips on how to examine the connections in greater detail. TODO LINK DOES NOT WORK + .. _conn_rules: -Connection Rules +Connection rules ---------------- -We here provide formal definitions of connectivity concepts for neuronal network models. These concepts encompass the basic connectivity rules illustrated above which are already commonly used by the computational neuroscience community. Beyond that, we discuss concepts to reflect some of the richness of anatomical brain connectivity and complement in particular non-spatial connectivity rules with rules for spatially organized connectivity. Much of the information on this page is based on the paper "Connectivity concepts in neuronal network modeling" [1]_. - -For each high-level connectivity rule, we give both an algorithmic construction rule and the resulting connectivity distribution. Modelers can use these definitions to succinctly specify connection rules in their studies. However, if details differ from our standard definitions, these details should still be specified. Furthermore, we suggest symbols that can be used to indicate the corresponding connectivity types in network diagrams and add the corresponding CSA expressions from [2]_. - -In the specification of connectivity concepts we use the following notations and definitions. Let :math:`\mathcal{S}=\{s_1,\ldots, s_{N_s}\}` be the ordered set of sources of cardinality :math:`N_s` and :math:`\mathcal{T}=\{t_1,\ldots, t_{N_t}\}` the set of targets of cardinality :math:`N_t`. Then the set of all possible directed edges between members of :math:`\mathcal{S}` and :math:`\mathcal{T}` is given by the Cartesian product :math:`\mathcal{E}_{ST}=\mathcal{S \times T}` of cardinality :math:`N_s\cdot N_t`. - -If the source and target populations are identical (:math:`\mathcal{S= T}`) a source can be its own target. We call such a self-connection an :ref:`autapse `. -If autapses are not allowed, the target set for any node :math:`i \in \mathcal{S}` is :math:`\mathcal{T=S} \setminus i`, with cardinality :math:`N_t=N_s-1`. -If there is more than one edge between a source and target (or from a node to itself), we call this a :ref:`multapse `. - -The :math:`{degree\ distribution}\ P(k)` is the distribution across nodes of the number of edges per node. In a directed network, the distribution of the number of edges going out of (into) a node is called the :math:`{out\!-\!degree} (in\!-\!degree)` distribution. The distributions given below describe the effect of applying a connection rule once to a given :math:`\mathcal{S}-\mathcal{T}` pair. - +We here elaborate on the connectivity concepts with details on :ref:`Autapses and multapses `, :ref:`Deterministic connection rules `, :ref:`Probabilistic connection rules `, and finally the :ref:`Connection generator interface ` (a method to create connections via CSA, the Connection Set Algebra [2]_). +Each rule is described with an illustration, a NEST code example, and mathematical details. +The mathematical details are extracted from the study on connectivity concepts [1]_ and contain a symbol which we recommend to use for describing this type of connectivity, the corresponding expression from CSA, and a formal definition with an algorithmic construction rule and the resulting connectivity distribution. -TODO EXPLAIN DEVIATIONS FROM CC +.. dropdown:: Mathematical details: General notations and definitions + | Let :math:`\mathcal{S}=\{s_1,\ldots, s_{N_s}\}` be the ordered set of sources of cardinality :math:`N_s` and :math:`\mathcal{T}=\{t_1,\ldots, t_{N_t}\}` the set of targets of cardinality :math:`N_t`. Then the set of all possible directed edges between members of :math:`\mathcal{S}` and :math:`\mathcal{T}` is given by the Cartesian product :math:`\mathcal{E}_{ST}=\mathcal{S \times T}` of cardinality :math:`N_s\cdot N_t`. + | + | If the source and target populations are identical (:math:`\mathcal{S= T}`) a source can be its own target. We call such a self-connection an :ref:`autapse `. If autapses are not allowed, the target set for any node :math:`i \in \mathcal{S}` is :math:`\mathcal{T=S} \setminus i`, with cardinality :math:`N_t=N_s-1`. If there is more than one edge between a source and target (or from a node to itself), we call this a :ref:`multapse `. + | + | The `degree distribution` :math:`P(k)` is the distribution across nodes of the number of edges per node. In a directed network, the distribution of the number of edges going out of (into) a node is called the `out-degree (in-degree)` distribution. The distributions given below describe the effect of applying a connection rule once to a given :math:`\mathcal{S}-\mathcal{T}` pair. -.. _multapse_autapse: +.. _autapse_multapse: Autapses and multapses ---------------------------------------------- @@ -128,24 +127,21 @@ Autapses and multapses :width: 450px :align: center +Autapses are self-connections of a node and multapses are multiple connections betweeen the same pair of nodes. -In the connection specification dictionary (containing the rule name and rule- -specific parameters), the additional switch ``allow_autapses`` (default: -``True``) can be set to allow or disallow self-connections. - -Likewise, ``allow_multapses`` (default: ``True``) can be used to specify if -multiple connections between the same pair of neurons are allowed or not. +In the connection specification dictionary ``conn_spec``, the additional switches ``allow_autapses`` (default: +``True``) and ``allow_multapses`` (default: ``True``) can be set to allow or disallow autapses and multapses. These switches are only effective during each single call to :py:func:`.Connect`. Calling the function multiple times with the same set of neurons might still lead to violations of these constraints, even though the -switches were set to `False` in each individual call. - +switches were set to ``False`` in each individual call. +.. _deterministic_rules: -Deterministic connectivity rules +Deterministic connection rules ------------------------------------------------- -Deterministic connectivity rules establish precisely defined sets of connections without any variability across network realizations. +Deterministic connection rules establish precisely defined sets of connections without any variability across network realizations. .. _one_to_one: @@ -157,7 +153,7 @@ One-to-one :align: center The `i`\-th node in ``S`` (source) is connected to the `i`\-th node in ``T`` (target). The -NodeCollections of ``S`` and ``T`` have to contain the same number of +``NodeCollections`` of ``S`` and ``T`` have to contain the same number of nodes. .. code-block:: python @@ -172,7 +168,7 @@ nodes. | **Symbol:** :math:`\delta` | **CSA:** :math:`\delta` | **Definition:** Each node in :math:`\mathcal{S}` is uniquely connected to one node in :math:`\mathcal{T}`. - | :math:`\mathcal{S}` and :math:`\mathcal{T}` must have identical cardinality :math:`N_s=N_t`, see :ref:`one to one `. Both sources and targets can be permuted independently even if :math:`\mathcal{S}=\mathcal{T}`. The in- and out-degree distributions are given by :math:`P(K)=\delta_{K,1}`, with Kronecker delta :math:`\delta_{i,j}=1` if :math:`i=j`, and zero otherwise. + | :math:`\mathcal{S}` and :math:`\mathcal{T}` must have identical cardinality :math:`N_s=N_t`. Both sources and targets can be permuted independently even if :math:`\mathcal{S}=\mathcal{T}`. The in- and out-degree distributions are given by :math:`P(K)=\delta_{K,1}`, with Kronecker delta :math:`\delta_{i,j}=1` if :math:`i=j`, and zero otherwise. .. _all_to_all: @@ -201,22 +197,38 @@ specified. | **Symbol:** :math:`\Omega` | **CSA:** :math:`\Omega` | **Definition:** Each node in :math:`\mathcal{S}` is connected to all nodes in :math:`\mathcal{T}`. - | The resulting edge set is the full edge set :math:`\mathcal{E}_\mathcal{ST}`. The in- and out-degree distributions are :math:`P_\text{in}(K)=\delta_{K,N_s}` for :math:`\mathcal{T}`, and :math:`P_\text{out}(K)=\delta_{K,N_t}` for :math:`\mathcal{S}`, respectively. An example is shown in :ref:`all to all `. + | The resulting edge set is the full edge set :math:`\mathcal{E}_\mathcal{ST}`. The in- and out-degree distributions are :math:`P_\text{in}(K)=\delta_{K,N_s}` for :math:`\mathcal{T}`, and :math:`P_\text{out}(K)=\delta_{K,N_t}` for :math:`\mathcal{S}`, respectively. + +Explicit connections +~~~~~~~~~~~~~~~~~~~~ + +Connections between explicit lists of source-target pairs can be realized in NEST by extracting the respective node ids from the ``NodeCollections`` and using the :ref:`One-to-one ` rule. + +.. code-block:: python + + n, m = 5, 5 + S = nest.Create('iaf_psc_alpha', n) # node ids: 1..5 + T = nest.Create('iaf_psc_alpha', m) # node ids: 6..10 + # source-target pairs: (3,8), (4,1), (1,9) + nest.Connect([3,4,1], [8,6,9], 'one_to_one') .. dropdown:: Mathematical details: Explicit connections | **Symbol:** X | **CSA:** Not applicable | **Definition:** Connections are established according to an explicit list of source-target pairs. - | Connectivity is defined by an explicit list of sources and targets, also known as *adjacency list*, as for instance derived from anatomical measurements. It is, hence, not the result of any specific algorithm. An alternative way of representing a fixed connectivity is by means of the *adjacency matrix A*, such that :math:`A_{ij}=1` if :math:`j` is connected to :math:`i`, and zero otherwise. We here adopt the common computational neuroscience practice to have the first index :math:`i` denote the target and the second index :math:`j` denote the source node. + | Connectivity is defined by an explicit list of sources and targets, also known as `adjacency list`, as for instance derived from anatomical measurements. It is, hence, not the result of any specific algorithm. An alternative way of representing a fixed connectivity is by means of the `adjacency matrix` :math:`A`, such that :math:`A_{ij}=1` if :math:`j` is connected to :math:`i`, and zero otherwise. We here adopt the common computational neuroscience practice to have the first index :math:`i` denote the target and the second index :math:`j` denote the source node. + -Probabilistic connectivity rules +.. _probabilistic_rules: + +Probabilistic connection rules ------------------------------------------------ -Probabilistic connectivity rules establish edges according to a probabilistic rule. Consequently, the exact connectivity varies with realizations. Still, such connectivity leads to specific expectation values of network characteristics, such as degree distributions or correlation structure. +Probabilistic connection rules establish edges according to a probabilistic rule. Consequently, the exact connectivity varies with realizations. Still, such connectivity leads to specific expectation values of network characteristics, such as degree distributions or correlation structure. .. _pairwise_bernoulli: -Pairwise bernoulli +Pairwise Bernoulli ~~~~~~~~~~~~~~~~~~ .. image:: ../static/img/Pairwise_bernoulli.png @@ -225,21 +237,22 @@ Pairwise bernoulli For each possible pair of nodes from ``S`` and ``T``, a connection is created with probability ``p``. +Note that multapses cannot be produced with this rule because each possible edge is visited only once, independent of how ``allow_multapses`` is set. .. code-block:: python n, m, p = 5, 5, 0.5 S= nest.Create('iaf_psc_alpha', n) T = nest.Create('iaf_psc_alpha', m) - conn_spec_dict = {'rule': 'pairwise_bernoulli', 'p': p} - nest.Connect(S, T, conn_spec_dict) + conn_spec = {'rule': 'pairwise_bernoulli', 'p': p} + nest.Connect(S, T, conn_spec) -.. dropdown:: Mathematical details: Pairwise bernoulli +.. dropdown:: Mathematical details: Pairwise Bernoulli | **Symbol:** :math:`p` | **CSA:** :math:`\rho(p)` | **Definition:** Each pair of nodes, with source in :math:`\mathcal{S}` and target in :math:`\mathcal{T}`, is connected with probability :math:`p`. - | In its standard form this rule cannot produce multapses since each possible edge is visited only once. If :math:`\mathcal{S=T}`, this concept is similar to Erdős-Rényi-graphs of the *constant probability* :math:`p-ensemble\ G(N,p)`---also called *binomial ensemble* [3]_; the only difference being that we here consider directed graphs, whereas the Erdős-Rényi model is undirected. The distribution of both in- and out-degrees is binomial, + | In its standard form this rule cannot produce multapses since each possible edge is visited only once. If :math:`\mathcal{S=T}`, this concept is similar to Erdős-Rényi-graphs of the `constant probability` `p-ensemble` :math:`G(N,p)`---also called `binomial ensemble` [3]_; the only difference being that we here consider directed graphs, whereas the Erdős-Rényi model is undirected. The distribution of both in- and out-degrees is binomial, .. math:: P(K_\text{in}=K)=\mathcal{B}(K|N_s,p):=\begin{pmatrix}N_s\\K\end{pmatrix}p^{K}(1-p)^{N_s-K} @@ -252,7 +265,7 @@ created with probability ``p``. respectively. The expected total number of edges equals :math:`\text{E}[N_\text{syn}]=pN_tN_s`. -Symmetric pairwise bernoulli +Symmetric pairwise Bernoulli ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ For each possible pair of nodes from ``S`` and ``T``, a connection is @@ -266,9 +279,9 @@ must be ``True``. n, m, p = 10, 12, 0.2 S = nest.Create('iaf_psc_alpha', n) T = nest.Create('iaf_psc_alpha', m) - conn_spec_dict = {'rule': 'symmetric_pairwise_bernoulli', 'p': p, - 'allow_autapses': False, 'make_symmetric': True} - nest.Connect(S, T, conn_spec_dict) + conn_spec = {'rule': 'symmetric_pairwise_bernoulli', 'p': p, + 'allow_autapses': False, 'make_symmetric': True} + nest.Connect(S, T, conn_spec) .. _fixed_total_number: @@ -285,23 +298,48 @@ Random, fixed total number The nodes in ``S`` are randomly connected with the nodes in ``T`` such that the total number of connections equals ``N``. +As multapses are per default allowed and possible with this rule, you can disallow them by adding ``'allow_multapses': False`` to the ``conn_dict``. + .. code-block:: python n, m, N = 5, 5, 10 S = nest.Create('iaf_psc_alpha', n) T = nest.Create('iaf_psc_alpha', m) - conn_spec_dict = {'rule': 'fixed_total_number', 'N': N} - # By default multapses are allowed, - # optionally if multapses are not allowed the following parameter must be set - # conn_spec_dict = {'rule': 'fixed_total_number', 'N': N, 'allow_autapses': False} - nest.Connect(S, T, conn_spec_dict) + conn_spec = {'rule': 'fixed_total_number', 'N': N} + nest.Connect(S, T, conn_spec) + +.. dropdown:: Mathematical details: Random, fixed total number with multapses + + | **Symbol:** :math:`\mathbf{\rho_N}(N_\text{syn})\mathbf{M}(\mathbb{N}_S \times \mathbb{N}_T)` + | **CSA:** :math:`\mathbf{\rho_N}(N_\text{syn})\mathbf{M}(\mathbb{N}_S \times \mathbb{N}_T)` + | **Definition:** :math:`N_\text{syn}\in\{0,\ldots,N_sN_t\}` edges are randomly drawn from the edge set :math:`\mathcal{E}_\mathcal{ST}` with replacement. + | If multapses are allowed, there are :math:`\begin{pmatrix}N_sN_t+N_\text{syn}-1\\N_\text{syn}\end{pmatrix}` possible networks for any given number :math:`N_\text{syn}\leq N_sN_t`. + | Because exactly :math:`N_\text{syn}` connections are distributed across :math:`N_t` targets with replacement, the joint in-degree distribution is multinomial, + + .. math:: + \begin{equation}\label{eq:randfixKm} + \begin{split} + &P(K_{\text{in},1}=K_1,\ldots,K_{\text{in},N_t}=K_{N_t})\\ + & \quad \quad \quad=\begin{cases} + \frac{N_\text{syn}!}{K_1!...K_{N_t}!} \,p^{N_\text{syn}} & \text{if}\,\,\sum_{j=1}^{N_t} K_j = N_\text{syn}\\ + \quad\quad 0 & \text{otherwise}\end{cases}\, + \end{split} + \end{equation} + + with :math:`p=1/N_t`. + + The out-degrees have an analogous multinomial distribution :math:`P(K_{\text{out},1}=K_1,\ldots,K_{\text{out},N_s}=K_{N_s})`, with :math:`p=1/N_s` and sources and targets switched. The marginal distributions are binomial distributions :math:`P(K_{\text{in},j}=K)= \mathcal{B}(K|N_\text{syn},1/N_t)` and :math:`P(K_{\text{out},j}=K)= \mathcal{B}(K|N_\text{syn},1/N_s)`, respectively. + + The :math:`\mathbf{M}`-operator of CSA should not be confused with the ":math:`M`" indicating that multapses are allowed in our symbolic notation. + + .. dropdown:: Mathematical details: Random, fixed total number without multapses | **Symbol:** :math:`N_\text{syn} \cancel{M}` | **CSA:** :math:`\mathbf{\rho_{N}}(N_\text{syn})(\mathbb{N}_S \times \mathbb{N}_T)` | **Definition:** :math:`N_\text{syn}\in\{0,\ldots,N_sN_t\}` edges are randomly drawn from the edge set :math:`\mathcal{E}_\mathcal{ST}` without replacement. - | For :math:`\mathcal{S}=\mathcal{T}` this is a directed graph generalization of Erdős-Rényi graphs of the *constant number of edges* :math:`N_\text{syn}`-ensemble :math:`G(N,N_\text{syn})` [4]_. There are :math:`\begin{pmatrix}N_s N_t\\N_\text{syn}\end{pmatrix}` possible networks for any given number :math:`N_\text{syn}\leq N_sN_t`, which all have the same probability. The resulting in- and out-degree distributions are multivariate hypergeometric distributions. + | For :math:`\mathcal{S}=\mathcal{T}` this is a directed graph generalization of Erdős-Rényi graphs of the `constant number of edges` :math:`N_\text{syn}`-ensemble :math:`G(N,N_\text{syn})` [4]_. There are :math:`\begin{pmatrix}N_s N_t\\N_\text{syn}\end{pmatrix}` possible networks for any given number :math:`N_\text{syn}\leq N_sN_t`, which all have the same probability. The resulting in- and out-degree distributions are multivariate hypergeometric distributions. .. math:: \begin{split} @@ -325,32 +363,6 @@ such that the total number of connections equals ``N``. with sources and targets switched for :math:`P(K_{\text{out},j}=K_j)`. - - -.. dropdown:: Mathematical details: Random, fixed total number with multapses - - | **Symbol:** :math:`\mathbf{\rho_N}(N_\text{syn})\mathbf{M}(\mathbb{N}_S \times \mathbb{N}_T)` - | **CSA:** :math:`\mathbf{\rho_N}(N_\text{syn})\mathbf{M}(\mathbb{N}_S \times \mathbb{N}_T)` - | **Definition:** :math:`N_\text{syn}\in\{0,\ldots,N_sN_t\}` edges are randomly drawn from the edge set :math:`\mathcal{E}_\mathcal{ST}` with replacement. - | If multapses are allowed, there are :math:`\begin{pmatrix}N_sN_t+N_\text{syn}-1\\N_\text{syn}\end{pmatrix}` possible networks for any given number :math:`N_\text{syn}\leq N_sN_t`. - | Because exactly :math:`N_\text{syn}` connections are distributed across :math:`N_t` targets with replacement, the joint in-degree distribution is multinomial, - - .. math:: - \begin{equation}\label{eq:randfixKm} - \begin{split} - &P(K_{\text{in},1}=K_1,\ldots,K_{\text{in},N_t}=K_{N_t})\\ - & \quad \quad \quad=\begin{cases} - \frac{N_\text{syn}!}{K_1!...K_{N_t}!} \,p^{N_\text{syn}} & \text{if}\,\,\sum_{j=1}^{N_t} K_j = N_\text{syn}\\ - \quad\quad 0 & \text{otherwise}\end{cases}\, - \end{split} - \end{equation} - - with :math:`p=1/N_t`. - - The out-degrees have an analogous multinomial distribution :math:`P(K_{\text{out},1}=K_1,\ldots,K_{\text{out},N_s}=K_{N_s})`, with :math:`p=1/N_s` and sources and targets switched. The marginal distributions are binomial distributions :math:`P(K_{\text{in},j}=K)= \mathcal{B}(K|N_\text{syn},1/N_t)` and :math:`P(K_{\text{out},j}=K)= \mathcal{B}(K|N_\text{syn},1/N_s)`, respectively. - - The :math:`\mathbf{M}`-operator of CSA should not be confused with the ":math:`M`" indicating that multapses are allowed in our symbolic notation. - .. _fixed_indegree: Random, fixed indegree @@ -363,20 +375,44 @@ Random, fixed indegree The nodes in ``S`` are randomly connected with the nodes in ``T`` such that each node in ``T`` has a fixed :hxt_ref:`indegree` of ``N``. +As multapses are per default allowed and possible with this rule, you can disallow them by adding ``'allow_multapses': False`` to the ``conn_dict``. + .. code-block:: python n, m, N = 5, 5, 2 S = nest.Create('iaf_psc_alpha', n) T = nest.Create('iaf_psc_alpha', m) - conn_spec_dict = {'rule': 'fixed_indegree', 'indegree': N} - # By default multapses are allowed, - # optionally if multapses are not allowed the following parameter must be set - # conn_spec_dict = {rule': 'fixed_indegree', 'indegree': N, 'allow_autapses': False} - nest.Connect(S, T, conn_spec_dict) + conn_spec = {'rule': 'fixed_indegree', 'indegree': N} + nest.Connect(S, T, conn_spec) + +.. dropdown:: Mathematical details: Random, fixed in-degree with multapses + + | **Symbol:** :math:`K_\text{in}, M` + | **CSA:** :math:`\mathbf{\rho_1}(K)\mathbf{M}(\mathbb{N}_S \times \mathbb{N}_T)` + | **Definition:** Each target node in :math:`\mathcal{T}` is connected to :math:`K_\text{in}` nodes in :math:`\mathcal{S}` randomly chosen with replacement. + | :math:`N_s` is the number of source nodes from which exactly :math:`K_\text{in}` connections are drawn with equal probability :math:`p=1/N_s` for each of the :math:`N_t` target nodes :math:`t_i\in\mathcal{T}`. The in-degree distribution is by definition :math:`P(K)=\delta_{K,K_\text{in}}`. To obtain the out-degree distribution, we observe that because multapses are allowed, drawing :math:`N_t` times :math:`K_{\text{in},i}=K_\text{in}` from :math:`\mathcal{S}` is equivalent to drawing :math:`N_t K_\text{in}` times with replacement from :math:`\mathcal{S}`. This procedure yields a multinomial distribution of the out-degrees :math:`K_{\text{out},j}` of source nodes :math:`s_j\in\mathcal{S}` [5]_, i.e., + + .. math:: + \begin{equation}\label{eq:rfin} + \begin{split} + &P(K_{\text{out},1}=K_1,\ldots,K_{\text{out},N_s}=K_{N_s})\\ + & \quad \quad \quad =\begin{cases} + \frac{(N_tK_\text{in})!}{K_1!...K_{N_s}!} p^{N_tK_\text{in}} & \text{if}\,\,\sum_{j=1}^{N_s} K_j = N_tK_\text{in}\\ + \quad\quad 0 & \text{otherwise}\end{cases} + \end{split} + \end{equation} + + The marginal distributions are binomial distributions + + .. math:: + \begin{equation}\label{eq:rfinmarg} + P(K_{\text{out},j}=K)= \mathcal{B}(K|N_tK_\text{in},1/N_s)\,. + \end{equation} + .. dropdown:: Mathematical details: Random, fixed in-degree without multapses - | **Symbol:** :math:`K_{in}, \cancel{M}` + | **Symbol:** :math:`K_\text{in}, \cancel{M}` | **CSA:** :math:`{\rho_1}(K)(\mathbb{N}_S \times \mathbb{N}_T)` | **Definition:** Each target node in :math:`\mathcal{T}` is connected to :math:`K_\text{in}` nodes in :math:`\mathcal{S}` randomly chosen without replacement. | The in-degree distribution is by definition :math:`P(K)=\delta_{K,K_\text{in}}`. To obtain the out-degree distribution, observe that after one target node has drawn its :math:`K_\text{out}` sources the joint probability distribution of out-degrees :math:`K_{\text{out},j}` is multivariate-hypergeometric such that @@ -406,31 +442,6 @@ that each node in ``T`` has a fixed :hxt_ref:`indegree` of ``N``. with :math:`\text{Ber}(p)` denoting the Bernoulli distribution with parameter :math:`p`, because :math:`K\in\{0,1\}`. The full joint distribution is the sum of :math:`N_t` independent instances of equation (1). -.. dropdown:: Mathematical details: Random, fixed in-degree with multapses - - | **Symbol:** :math:`K_{in}, M` - | **CSA:** :math:`\mathbf{\rho_1}(K)\mathbf{M}(\mathbb{N}_S \times \mathbb{N}_T)` - | **Definition:** Each target node in :math:`\mathcal{T}` is connected to :math:`K_\text{in}` nodes in :math:`\mathcal{S}` randomly chosen with replacement. - | :math:`N_s` is the number of source nodes from which exactly :math:`K_\text{in}` connections are drawn with equal probability :math:`p=1/N_s` for each of the :math:`N_t` target nodes :math:`t_i\in\mathcal{T}`. The in-degree distribution is by definition :math:`P(K)=\delta_{K,K_\text{in}}`. To obtain the out-degree distribution, we observe that because multapses are allowed, drawing :math:`N_t` times :math:`K_{\text{in},i}=K_\text{in}` from :math:`\mathcal{S}` is equivalent to drawing :math:`N_t K_\text{in}` times with replacement from :math:`\mathcal{S}`. This procedure yields a multinomial distribution of the out-degrees :math:`K_{\text{out},j}` of source nodes :math:`s_j\in\mathcal{S}` [5]_, i.e., - - .. math:: - \begin{equation}\label{eq:rfin} - \begin{split} - &P(K_{\text{out},1}=K_1,\ldots,K_{\text{out},N_s}=K_{N_s})\\ - & \quad \quad \quad =\begin{cases} - \frac{(N_tK_\text{in})!}{K_1!...K_{N_s}!} p^{N_tK_\text{in}} & \text{if}\,\,\sum_{j=1}^{N_s} K_j = N_tK_\text{in}\\ - \quad\quad 0 & \text{otherwise}\end{cases} - \end{split} - \end{equation} - - The marginal distributions are binomial distributions - - .. math:: - \begin{equation}\label{eq:rfinmarg} - P(K_{\text{out},j}=K)= \mathcal{B}(K|N_tK_\text{in},1/N_s)\,. - \end{equation} - - .. _fixed_outdegree: @@ -444,46 +455,35 @@ Random, fixed outdegree The nodes in ``S`` are randomly connected with the nodes in ``T`` such that each node in ``S`` has a fixed :hxt_ref:`outdegree` of ``N``. +As multapses are per default allowed and possible with this rule, you can disallow them by adding ``'allow_multapses': False`` to the ``conn_dict``. + .. code-block:: python n, m, N = 5, 5, 2 S = nest.Create('iaf_psc_alpha', n) T = nest.Create('iaf_psc_alpha', m) - conn_spec_dict = {'rule': 'fixed_outdegree', 'outdegree': N} - - # By default multapses are allowed, - # optionally if multapses are not allowed the following parameter must be set - # conn_spec_dict = {'rule': 'fixed_outdegree', 'outdegree': N, 'allow_autapses': False} - nest.Connect(S, T, conn_spec_dict) + conn_spec = {'rule': 'fixed_outdegree', 'outdegree': N} + nest.Connect(S, T, conn_spec) -.. dropdown:: Mathematical details: Random, fixed out-degree without multapses - - | **Symbol:** :math:`K_{out},\cancel{M}` - | **CSA:** :math:`\mathbf{\rho_0}(K)(\mathbb{N}_S \times \mathbb{N}_T)` - | **Definition:** Each source node in :math:`S` is connected to :math:`K_\text{out}` nodes in :math:`\mathcal{T}` randomly chosen without replacement. - | The out-degree distribution is by definition :math:`P(K)=\delta_{K,K_\text{out}}`, while the in-degree distribution is obtained by switching source and target indices, and replacing :math:`K_\text{out}` with :math:`K_\text{in}` in equation (2) from :ref:`Random, fixed in-degree without multapses `. - - .. dropdown:: Mathematical details: Random, fixed out-degree with multapses - | **Symbol:** :math:`K_{out}, M` + | **Symbol:** :math:`K_text{out}, M` | **CSA:** :math:`\mathbf{\rho_0}(K)\mathbf{M}(\mathbb{N}_S \times \mathbb{N}_T)` | **Definition:** Each source node in :math:`\mathcal{S}` is connected to :math:`K_\text{out}` nodes in :math:`\mathcal{T}` randomly chosen with replacement. | By definition, the out-degree distribution is a :math:`P(K)=\delta_{K,K_\text{out}}`. The respective in-degree distribution and marginal distributions are obtained by switching source and target indices, and replacing :math:`K_\text{out}` with :math:`K_\text{in}` in equation from :ref:`Random, fixed in-degree with multapses ` [5]_. +.. dropdown:: Mathematical details: Random, fixed out-degree without multapses - - - - - - + | **Symbol:** :math:`K_text{out},\cancel{M}` + | **CSA:** :math:`\mathbf{\rho_0}(K)(\mathbb{N}_S \times \mathbb{N}_T)` + | **Definition:** Each source node in :math:`S` is connected to :math:`K_\text{out}` nodes in :math:`\mathcal{T}` randomly chosen without replacement. + | The out-degree distribution is by definition :math:`P(K)=\delta_{K,K_\text{out}}`, while the in-degree distribution is obtained by switching source and target indices, and replacing :math:`K_\text{out}` with :math:`K_\text{in}` in equation (2) from :ref:`Random, fixed in-degree without multapses `. .. _conn_builder_conngen: -Conngen -~~~~~~~ +Connection generator interface +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. admonition:: Availability @@ -505,9 +505,8 @@ indices can be specified under the key ``params_map``. Alternatively, all synapse parameters can be specified using the synapse specification argument to ``Connect()``. -The following listing shows an example for using the `Connection-Set -Algebra `_ in NEST via the Connection -Generator Interface and randomly connects 10% of the neurons from +The following listing shows an example for using the `Connection Set Algebra `_ [2]_ in NEST via the Connection +generator interface and randomly connects 10% of the neurons from ``A`` to the neurons in ``B``, each connection having a weight of 10000.0 pA and a delay of 1.0 ms: @@ -523,8 +522,8 @@ Generator Interface and randomly connects 10% of the neurons from # Map weight and delay indices to values from cg params_map = {'weight': 0, 'delay': 1} - conn_spec_dict = {'rule': 'conngen', 'cg': cg, 'params_map': params_map} - nest.Connect(A, B, conn_spec_dict) + conn_spec = {'rule': 'conngen', 'cg': cg, 'params_map': params_map} + nest.Connect(A, B, conn_spec) References From 13696906eaf50d73804898cb1d80287be624dd3a Mon Sep 17 00:00:00 2001 From: Johanna Senk Date: Thu, 16 Nov 2023 14:27:45 +0100 Subject: [PATCH 06/37] More edits on connection management --- .../synapses/connection_management.rst | 75 ++++++++++--------- 1 file changed, 41 insertions(+), 34 deletions(-) diff --git a/doc/htmldoc/synapses/connection_management.rst b/doc/htmldoc/synapses/connection_management.rst index 0a6b9cfedf..0808c0af63 100644 --- a/doc/htmldoc/synapses/connection_management.rst +++ b/doc/htmldoc/synapses/connection_management.rst @@ -6,63 +6,70 @@ Connection management .. grid:: 4 :gutter: 1 - .. card:: - :link: one_to_one + .. grid-item-card:: + :link: autapse_multapse :link-type: ref - .. image:: ../static/img/One_to_one_H.png + .. image:: ../static/img/Autapse_H.png - .. card:: - :link: all_to_all + .. grid-item-card:: + :link: autapse_multapse :link-type: ref - .. image:: ../static/img/All_to_all_H.png + .. image:: ../static/img/Multapse_H.png - .. card:: - :link: fixed_indegree + .. grid-item-card:: + :link: one_to_one :link-type: ref - .. image:: ../static/img/Fixed_indegree_H.png + .. image:: ../static/img/One_to_one_H.png - .. card:: - :link: fixed_outdegree + .. grid-item-card:: + :link: all_to_all :link-type: ref - .. image:: ../static/img/Fixed_outdegree_H.png + .. image:: ../static/img/All_to_all_H.png .. grid:: 4 :gutter: 1 - .. card:: + .. grid-item-card:: + :link: pairwise_bernoulli + :link-type: ref + + .. image:: ../static/img/Pairwise_bernoulli_H.png + + + .. grid-item-card:: :link: fixed_total_number :link-type: ref .. image:: ../static/img/Fixed_total_number_H.png + - .. card:: - :link: pairwise_bernoulli + .. grid-item-card:: + :link: fixed_indegree :link-type: ref - .. image:: ../static/img/Pairwise_bernoulli_H.png + .. image:: ../static/img/Fixed_indegree_H.png - .. card:: - :link: autapse_multapse + .. grid-item-card:: + :link: fixed_outdegree :link-type: ref - .. image:: ../static/img/Autapse_H.png + .. image:: ../static/img/Fixed_outdegree_H.png - .. card:: - :link: autapse_multapse - :link-type: ref - .. image:: ../static/img/Multapse_H.png +.. class:: center Basic connection rules commonly used in the computational neuroscience community. For more details, go to the section :ref:`Connection rules ` or just click on one of the illustrations. -This documentation not only describes how to define network connectivity in NEST, but also represents a living reference for the connection rules defined in the article "Connectivity concepts in neuronal network modeling" [1]_. -The same article also introduces a graphical notation for neuronal network diagrams which is curated in the documentation of NEST Desktop. TODO ADD LINK -For spatially structured networks, refer to TODO. +This documentation not only describes how to define network connectivity in NEST, but also represents a living reference for the connection rules defined in the article "Connectivity concepts in neuronal network modeling" [1]_. Deviations from the rules defined in the publication are highlighted in green. + +.. The same article also introduces a graphical notation for neuronal network diagrams which is curated in the documentation of NEST Desktop. TODO ADD LINK WHEN IT EXISTS + +:ref:`Spatially structured networks ` are described on a separate page. We use the term `connection` to mean a single, atomic edge between network nodes (i.e., neurons or devices). A `projection` is a group of edges that connects groups of nodes with similar properties (i.e., populations). @@ -98,7 +105,7 @@ done using the corresponding kernel attribute: print(nest.num_connections) -Have a look at the section :ref:`inspecting_connections` to get more tips on how to examine the connections in greater detail. TODO LINK DOES NOT WORK +Have a look at the section :ref:`handling_connections` to get more tips on how to examine the connections in greater detail. .. _conn_rules: @@ -209,7 +216,7 @@ Connections between explicit lists of source-target pairs can be realized in NES n, m = 5, 5 S = nest.Create('iaf_psc_alpha', n) # node ids: 1..5 T = nest.Create('iaf_psc_alpha', m) # node ids: 6..10 - # source-target pairs: (3,8), (4,1), (1,9) + # source-target pairs: (3,8), (4,1), (1,9) nest.Connect([3,4,1], [8,6,9], 'one_to_one') .. dropdown:: Mathematical details: Explicit connections @@ -365,7 +372,7 @@ As multapses are per default allowed and possible with this rule, you can disall .. _fixed_indegree: -Random, fixed indegree +Random, fixed in-degree ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. image:: ../static/img/Fixed_indegree.png @@ -373,7 +380,7 @@ Random, fixed indegree :align: center The nodes in ``S`` are randomly connected with the nodes in ``T`` such -that each node in ``T`` has a fixed :hxt_ref:`indegree` of ``N``. +that each node in ``T`` has a fixed ``indegree`` of ``N``. As multapses are per default allowed and possible with this rule, you can disallow them by adding ``'allow_multapses': False`` to the ``conn_dict``. @@ -445,7 +452,7 @@ As multapses are per default allowed and possible with this rule, you can disall .. _fixed_outdegree: -Random, fixed outdegree +Random, fixed out-degree ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. image:: ../static/img/Fixed_outdegree.png @@ -453,7 +460,7 @@ Random, fixed outdegree :align: center The nodes in ``S`` are randomly connected with the nodes in ``T`` such -that each node in ``S`` has a fixed :hxt_ref:`outdegree` of ``N``. +that each node in ``S`` has a fixed ``outdegree`` of ``N``. As multapses are per default allowed and possible with this rule, you can disallow them by adding ``'allow_multapses': False`` to the ``conn_dict``. @@ -492,7 +499,7 @@ Connection generator interface To allow the generation of connectivity by means of an external library, NEST supports the Connection Generator Interface [6]_. For -more details on this interface, see the Git repository of `libneurosim +more details on this interface, see the git repository of `libneurosim `_. In contrast to the other rules for creating connections, this rule @@ -505,7 +512,7 @@ indices can be specified under the key ``params_map``. Alternatively, all synapse parameters can be specified using the synapse specification argument to ``Connect()``. -The following listing shows an example for using the `Connection Set Algebra `_ [2]_ in NEST via the Connection +The following listing shows an example for using CSA (`Connection Set Algebra `_ [2]_) in NEST via the Connection generator interface and randomly connects 10% of the neurons from ``A`` to the neurons in ``B``, each connection having a weight of 10000.0 pA and a delay of 1.0 ms: From 6a688d832f8fa4831e68f4fa2ec5302f4901e669 Mon Sep 17 00:00:00 2001 From: Johanna Senk Date: Thu, 16 Nov 2023 14:38:39 +0100 Subject: [PATCH 07/37] Update connection rule names in synapse specifications --- .../synapses/synapse_specification.rst | 73 ++++++++++--------- 1 file changed, 37 insertions(+), 36 deletions(-) diff --git a/doc/htmldoc/synapses/synapse_specification.rst b/doc/htmldoc/synapses/synapse_specification.rst index a2b9e8b695..547dc14ff8 100644 --- a/doc/htmldoc/synapses/synapse_specification.rst +++ b/doc/htmldoc/synapses/synapse_specification.rst @@ -60,13 +60,28 @@ will be applied to all connections created in the current Array parameters ~~~~~~~~~~~~~~~~ -Array parameters can be used with the rules ``all_to_all``, -``fixed_indegree``, ``fixed_outdegree``, ``fixed_total_number`` and -``one_to_one``. The arrays can be specified as NumPy arrays or Python +Array parameters can be used with the rules ``one_to_one``, ``all_to_all``, +``fixed_total_number``, ``fixed_indegree``, and ``fixed_outdegree``. +For details on connection rules, see :ref:`connection_management`. +The arrays can be specified as NumPy arrays or Python lists. As with the scalar parameters, all parameters have to be specified as arrays of the correct type. -rule: all-to-all +Rule: One-to-one +^^^^^^^^^^^^^^^^ + +For rule ``one_to_one`` the array must have the same length as there +are nodes in ``A`` and ``B``. + +.. code-block:: python + + A = nest.Create('iaf_psc_alpha', 2) + B = nest.Create('spike_recorder', 2) + conn_spec_dict = {'rule': 'one_to_one'} + syn_spec_dict = {'weight': [1.2, -3.5]} + nest.Connect(A, B, conn_spec_dict, syn_spec_dict) + +Rule: All-to-all ^^^^^^^^^^^^^^^^ When connecting with rule ``all_to_all``, the array parameter must @@ -79,8 +94,22 @@ have dimension `len(B) x len(A)`. syn_spec_dict = {'weight': [[1.2, -3.5, 2.5], [0.4, -0.2, 0.7]]} nest.Connect(A, B, syn_spec=syn_spec_dict) -rule: fixed indegree -^^^^^^^^^^^^^^^^^^^^ +Rule: Random, fixed total number +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +For rule ``fixed_total_number``, the array has to be same the length as the +number of connections ``N``. + +.. code-block:: python + + A = nest.Create('iaf_psc_alpha', 3) + B = nest.Create('iaf_psc_alpha', 4) + conn_spec_dict = {'rule': 'fixed_total_number', 'N': 4} + syn_spec_dict = {'weight': [1.2, -3.5, 0.4, -0.2]} + nest.Connect(A, B, conn_spec_dict, syn_spec_dict) + +Rule: Random, fixed in-degree +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ For rule ``fixed_indegree`` the array has to be a two-dimensional NumPy array or Python list with shape ``(len(B), indegree)``, where @@ -97,8 +126,8 @@ of the identity of the source neurons. syn_spec_dict = {'weight': [[1.2, -3.5],[0.4, -0.2],[0.6, 2.2]]} nest.Connect(A, B, conn_spec_dict, syn_spec_dict) -rule: fixed outdegree -^^^^^^^^^^^^^^^^^^^^^ +Rule: Random, fixed out-degree +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ For rule ``fixed_outdegree`` the array has to be a two-dimensional NumPy array or Python list with shape ``(len(A), outdegree)``, where @@ -115,34 +144,6 @@ regardless of the identity of the target neuron. syn_spec_dict = {'weight': [[1.2, -3.5, 0.4], [-0.2, 0.6, 2.2]]} nest.Connect(A, B, conn_spec_dict, syn_spec_dict) -rule: fixed total number -^^^^^^^^^^^^^^^^^^^^^^^^ - -For rule ``fixed_total_number``, the array has to be same the length as the -number of connections ``N``. - -.. code-block:: python - - A = nest.Create('iaf_psc_alpha', 3) - B = nest.Create('iaf_psc_alpha', 4) - conn_spec_dict = {'rule': 'fixed_total_number', 'N': 4} - syn_spec_dict = {'weight': [1.2, -3.5, 0.4, -0.2]} - nest.Connect(A, B, conn_spec_dict, syn_spec_dict) - -rule: one-to-one -^^^^^^^^^^^^^^^^ - -For rule ``one_to_one`` the array must have the same length as there -are nodes in ``A`` and ``B``. - -.. code-block:: python - - A = nest.Create('iaf_psc_alpha', 2) - B = nest.Create('spike_recorder', 2) - conn_spec_dict = {'rule': 'one_to_one'} - syn_spec_dict = {'weight': [1.2, -3.5]} - nest.Connect(A, B, conn_spec_dict, syn_spec_dict) - .. _dist_params: Expressions as parameters From f47bf319a8aed0427ae990a226c2e1da267481f2 Mon Sep 17 00:00:00 2001 From: Johanna Senk Date: Thu, 16 Nov 2023 15:31:08 +0100 Subject: [PATCH 08/37] Final edits --- .../synapses/connection_management.rst | 28 +++++++++---------- doc/htmldoc/synapses/handling_connections.rst | 7 +++-- .../synapses/synapse_specification.rst | 24 ++++++++-------- doc/htmldoc/understand_index.rst | 2 ++ 4 files changed, 34 insertions(+), 27 deletions(-) diff --git a/doc/htmldoc/synapses/connection_management.rst b/doc/htmldoc/synapses/connection_management.rst index 0808c0af63..633d4a02aa 100644 --- a/doc/htmldoc/synapses/connection_management.rst +++ b/doc/htmldoc/synapses/connection_management.rst @@ -59,17 +59,17 @@ Connection management .. image:: ../static/img/Fixed_outdegree_H.png +.. card:: + :shadow: none + :text-align: center -.. class:: center - -Basic connection rules commonly used in the computational neuroscience community. -For more details, go to the section :ref:`Connection rules ` or just click on one of the illustrations. + Basic connection rules commonly used in the computational neuroscience community. For more details, go to the section :ref:`conn_rules` or just click on one of the illustrations. This documentation not only describes how to define network connectivity in NEST, but also represents a living reference for the connection rules defined in the article "Connectivity concepts in neuronal network modeling" [1]_. Deviations from the rules defined in the publication are highlighted in green. .. The same article also introduces a graphical notation for neuronal network diagrams which is curated in the documentation of NEST Desktop. TODO ADD LINK WHEN IT EXISTS -:ref:`Spatially structured networks ` are described on a separate page. +:ref:`spatial_networks` are described on a separate page. We use the term `connection` to mean a single, atomic edge between network nodes (i.e., neurons or devices). A `projection` is a group of edges that connects groups of nodes with similar properties (i.e., populations). @@ -84,16 +84,16 @@ Projections are created in NEST with the :py:func:`.Connect` function: nest.Connect(pre, post, conn_spec, syn_spec) In the simplest case, the function just takes the ``NodeCollections`` ``pre`` and ``post``, defining the nodes of -origin (`sources`) and termination (`targets`) for the connections to be established with the default rule ``all-to-all`` and the synapse model :hxt_ref:`static_synapse`. +origin (`sources`) and termination (`targets`) for the connections to be established with the default rule ``all-to-all`` and the synapse model :ref:`static_synapse`. Other connectivity patterns can be achieved by explicitly specifying the connection rule with the connectivity specification dictionary ``conn_spec`` which expects a ``rule`` alongside additional rule-specific parameters. Rules that do not require parameters can be directly provided as string instead of the dictionary; for example, ``nest.Connect(pre, post, 'one_to_one')``. Examples of parameters might be in- and out-degrees, or the probability for establishing a connection. -All available rules are described in the section :ref:`Connection rules ` below. +All available rules are described in the section :ref:`conn_rules` below. Properties of individual connections (i.e., synapses) can be set via the synapse specification dictionary ``syn_spec``. Parameters like the synaptic weight or delay can be either set values or drawn and combined flexibly from random distributions. -For details on synapse models and their parameters refer to :ref:`Synapse specification `.Note that is also possible to define multiple projections with different synapse properties in the same :py:func:`.Connect` call (see :ref:`collocated synapses `). +For details on synapse models and their parameters refer to :ref:`synapse_spec`.Note that is also possible to define multiple projections with different synapse properties in the same :py:func:`.Connect` call (see :ref:`collocated_synapses`). By using the keyword variant ``nest.Connect(pre, post, syn_spec=syn_spec)``, the `conn_spec` can be omitted in the call to :py:func:`.Connect` and will just take on the default value ``all-to-all``. @@ -113,7 +113,7 @@ Have a look at the section :ref:`handling_connections` to get more tips on how t Connection rules ---------------- -We here elaborate on the connectivity concepts with details on :ref:`Autapses and multapses `, :ref:`Deterministic connection rules `, :ref:`Probabilistic connection rules `, and finally the :ref:`Connection generator interface ` (a method to create connections via CSA, the Connection Set Algebra [2]_). +We here elaborate on the connectivity concepts with details on :ref:`autapse_multapse`, :ref:`deterministic_rules`, :ref:`probabilistic_rules`, and finally the :ref:`conn_builder_conngen` (a method to create connections via CSA, the Connection Set Algebra [2]_). Each rule is described with an illustration, a NEST code example, and mathematical details. The mathematical details are extracted from the study on connectivity concepts [1]_ and contain a symbol which we recommend to use for describing this type of connectivity, the corresponding expression from CSA, and a formal definition with an algorithmic construction rule and the resulting connectivity distribution. @@ -209,7 +209,7 @@ specified. Explicit connections ~~~~~~~~~~~~~~~~~~~~ -Connections between explicit lists of source-target pairs can be realized in NEST by extracting the respective node ids from the ``NodeCollections`` and using the :ref:`One-to-one ` rule. +Connections between explicit lists of source-target pairs can be realized in NEST by extracting the respective node ids from the ``NodeCollections`` and using the :ref:`one_to_one` rule. .. code-block:: python @@ -474,17 +474,17 @@ As multapses are per default allowed and possible with this rule, you can disall .. dropdown:: Mathematical details: Random, fixed out-degree with multapses - | **Symbol:** :math:`K_text{out}, M` + | **Symbol:** :math:`K_\text{out}, M` | **CSA:** :math:`\mathbf{\rho_0}(K)\mathbf{M}(\mathbb{N}_S \times \mathbb{N}_T)` | **Definition:** Each source node in :math:`\mathcal{S}` is connected to :math:`K_\text{out}` nodes in :math:`\mathcal{T}` randomly chosen with replacement. - | By definition, the out-degree distribution is a :math:`P(K)=\delta_{K,K_\text{out}}`. The respective in-degree distribution and marginal distributions are obtained by switching source and target indices, and replacing :math:`K_\text{out}` with :math:`K_\text{in}` in equation from :ref:`Random, fixed in-degree with multapses ` [5]_. + | By definition, the out-degree distribution is a :math:`P(K)=\delta_{K,K_\text{out}}`. The respective in-degree distribution and marginal distributions are obtained by switching source and target indices, and replacing :math:`K_\text{out}` with :math:`K_\text{in}` in equation from :ref:`fixed_indegree` [5]_. .. dropdown:: Mathematical details: Random, fixed out-degree without multapses - | **Symbol:** :math:`K_text{out},\cancel{M}` + | **Symbol:** :math:`K_\text{out},\cancel{M}` | **CSA:** :math:`\mathbf{\rho_0}(K)(\mathbb{N}_S \times \mathbb{N}_T)` | **Definition:** Each source node in :math:`S` is connected to :math:`K_\text{out}` nodes in :math:`\mathcal{T}` randomly chosen without replacement. - | The out-degree distribution is by definition :math:`P(K)=\delta_{K,K_\text{out}}`, while the in-degree distribution is obtained by switching source and target indices, and replacing :math:`K_\text{out}` with :math:`K_\text{in}` in equation (2) from :ref:`Random, fixed in-degree without multapses `. + | The out-degree distribution is by definition :math:`P(K)=\delta_{K,K_\text{out}}`, while the in-degree distribution is obtained by switching source and target indices, and replacing :math:`K_\text{out}` with :math:`K_\text{in}` in equation (2) from :ref:`fixed_indegree`. .. _conn_builder_conngen: diff --git a/doc/htmldoc/synapses/handling_connections.rst b/doc/htmldoc/synapses/handling_connections.rst index c72c8fabf9..0a87b20df8 100644 --- a/doc/htmldoc/synapses/handling_connections.rst +++ b/doc/htmldoc/synapses/handling_connections.rst @@ -1,7 +1,10 @@ .. _handling_connections: -How to handle connections (synapses) -===================================== +How to handle created connections +================================= + +Here, we describe how to inspect and modify already created connections. +For creating new connections, refer to :ref:`connection_management`, and for parameterizing synapses, see :ref:`synapse_spec`. Just like a NodeCollection is a container for node IDs, a SynapseCollection is a container for connections. In NEST 3, when you call :py:func:`.GetConnections` a diff --git a/doc/htmldoc/synapses/synapse_specification.rst b/doc/htmldoc/synapses/synapse_specification.rst index 547dc14ff8..649f835fa1 100644 --- a/doc/htmldoc/synapses/synapse_specification.rst +++ b/doc/htmldoc/synapses/synapse_specification.rst @@ -1,8 +1,10 @@ .. _synapse_spec: -Synapse Specification +Synapse specification ======================== +This page is about parameterizing synapses. See :ref:`connection_management` for specifying connections, and :ref:`handling_connections` for inspecting and modifying ``SynapseCollections``. + The synapse properties can be given as just the name of the desired synapse model as a string, as a dictionary specifying synapse model and parameters, or as a ``CollocatedSynapse`` object creating @@ -67,8 +69,8 @@ The arrays can be specified as NumPy arrays or Python lists. As with the scalar parameters, all parameters have to be specified as arrays of the correct type. -Rule: One-to-one -^^^^^^^^^^^^^^^^ +One-to-one +^^^^^^^^^^ For rule ``one_to_one`` the array must have the same length as there are nodes in ``A`` and ``B``. @@ -81,8 +83,8 @@ are nodes in ``A`` and ``B``. syn_spec_dict = {'weight': [1.2, -3.5]} nest.Connect(A, B, conn_spec_dict, syn_spec_dict) -Rule: All-to-all -^^^^^^^^^^^^^^^^ +All-to-all +^^^^^^^^^^ When connecting with rule ``all_to_all``, the array parameter must have dimension `len(B) x len(A)`. @@ -94,8 +96,8 @@ have dimension `len(B) x len(A)`. syn_spec_dict = {'weight': [[1.2, -3.5, 2.5], [0.4, -0.2, 0.7]]} nest.Connect(A, B, syn_spec=syn_spec_dict) -Rule: Random, fixed total number -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Random, fixed total number +^^^^^^^^^^^^^^^^^^^^^^^^^^ For rule ``fixed_total_number``, the array has to be same the length as the number of connections ``N``. @@ -108,8 +110,8 @@ number of connections ``N``. syn_spec_dict = {'weight': [1.2, -3.5, 0.4, -0.2]} nest.Connect(A, B, conn_spec_dict, syn_spec_dict) -Rule: Random, fixed in-degree -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Random, fixed in-degree +^^^^^^^^^^^^^^^^^^^^^^^ For rule ``fixed_indegree`` the array has to be a two-dimensional NumPy array or Python list with shape ``(len(B), indegree)``, where @@ -126,8 +128,8 @@ of the identity of the source neurons. syn_spec_dict = {'weight': [[1.2, -3.5],[0.4, -0.2],[0.6, 2.2]]} nest.Connect(A, B, conn_spec_dict, syn_spec_dict) -Rule: Random, fixed out-degree -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Random, fixed out-degree +^^^^^^^^^^^^^^^^^^^^^^^^ For rule ``fixed_outdegree`` the array has to be a two-dimensional NumPy array or Python list with shape ``(len(A), outdegree)``, where diff --git a/doc/htmldoc/understand_index.rst b/doc/htmldoc/understand_index.rst index 76df941e31..729d56d338 100644 --- a/doc/htmldoc/understand_index.rst +++ b/doc/htmldoc/understand_index.rst @@ -21,6 +21,8 @@ Here you can dive into various topics about NEST. * :ref:`connection_management` + * :ref:`synapse_spec` + * :ref:`handling_connections` * :ref:`sim_gap_junctions` From 27a68fe4b55d35a97839831edfa556f404ec0150 Mon Sep 17 00:00:00 2001 From: clemensk0 <137566771+clemensk0@users.noreply.github.com> Date: Thu, 16 Nov 2023 15:56:38 +0100 Subject: [PATCH 09/37] Update connection_management.rst --- doc/htmldoc/synapses/connection_management.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/htmldoc/synapses/connection_management.rst b/doc/htmldoc/synapses/connection_management.rst index 633d4a02aa..304e71c900 100644 --- a/doc/htmldoc/synapses/connection_management.rst +++ b/doc/htmldoc/synapses/connection_management.rst @@ -60,7 +60,7 @@ Connection management .. image:: ../static/img/Fixed_outdegree_H.png .. card:: - :shadow: none + :shadow: none :text-align: center Basic connection rules commonly used in the computational neuroscience community. For more details, go to the section :ref:`conn_rules` or just click on one of the illustrations. From 90e22ea5b6706bd0cddaea4bdb11bde54f59d638 Mon Sep 17 00:00:00 2001 From: jessica-mitchell Date: Fri, 17 Nov 2023 11:22:22 +0100 Subject: [PATCH 10/37] Update doc/htmldoc/synapses/connection_management.rst --- doc/htmldoc/synapses/connection_management.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/htmldoc/synapses/connection_management.rst b/doc/htmldoc/synapses/connection_management.rst index 304e71c900..ddfa3101a6 100644 --- a/doc/htmldoc/synapses/connection_management.rst +++ b/doc/htmldoc/synapses/connection_management.rst @@ -93,7 +93,7 @@ All available rules are described in the section :ref:`conn_rules` below. Properties of individual connections (i.e., synapses) can be set via the synapse specification dictionary ``syn_spec``. Parameters like the synaptic weight or delay can be either set values or drawn and combined flexibly from random distributions. -For details on synapse models and their parameters refer to :ref:`synapse_spec`.Note that is also possible to define multiple projections with different synapse properties in the same :py:func:`.Connect` call (see :ref:`collocated_synapses`). +For details on synapse models and their parameters refer to :ref:`synapse_spec`. Note that is also possible to define multiple projections with different synapse properties in the same :py:func:`.Connect` call (see :ref:`collocated_synapses`). By using the keyword variant ``nest.Connect(pre, post, syn_spec=syn_spec)``, the `conn_spec` can be omitted in the call to :py:func:`.Connect` and will just take on the default value ``all-to-all``. From f4164c00c7b8d551442ac34c6b338c7deff1ec74 Mon Sep 17 00:00:00 2001 From: jessica-mitchell Date: Mon, 20 Nov 2023 09:15:46 +0100 Subject: [PATCH 11/37] Update doc/htmldoc/synapses/connection_management.rst --- doc/htmldoc/synapses/connection_management.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/htmldoc/synapses/connection_management.rst b/doc/htmldoc/synapses/connection_management.rst index ddfa3101a6..674b08ec69 100644 --- a/doc/htmldoc/synapses/connection_management.rst +++ b/doc/htmldoc/synapses/connection_management.rst @@ -113,7 +113,7 @@ Have a look at the section :ref:`handling_connections` to get more tips on how t Connection rules ---------------- -We here elaborate on the connectivity concepts with details on :ref:`autapse_multapse`, :ref:`deterministic_rules`, :ref:`probabilistic_rules`, and finally the :ref:`conn_builder_conngen` (a method to create connections via CSA, the Connection Set Algebra [2]_). +Here we elaborate on the connectivity concepts with details on :ref:`autapse_multapse`, :ref:`deterministic_rules`, :ref:`probabilistic_rules`, and finally the :ref:`conn_builder_conngen` (a method to create connections via CSA, the Connection Set Algebra [2]_). Each rule is described with an illustration, a NEST code example, and mathematical details. The mathematical details are extracted from the study on connectivity concepts [1]_ and contain a symbol which we recommend to use for describing this type of connectivity, the corresponding expression from CSA, and a formal definition with an algorithmic construction rule and the resulting connectivity distribution. From 0004962e5905bdf26b6d080148e38ce175dcea11 Mon Sep 17 00:00:00 2001 From: Johanna Senk Date: Tue, 5 Dec 2023 11:51:26 +0100 Subject: [PATCH 12/37] Add cross-reference to tripartite rule --- .../synapses/connection_management.rst | 98 ++++++++++--------- 1 file changed, 51 insertions(+), 47 deletions(-) diff --git a/doc/htmldoc/synapses/connection_management.rst b/doc/htmldoc/synapses/connection_management.rst index 8d3b2e2be4..a8087560b3 100644 --- a/doc/htmldoc/synapses/connection_management.rst +++ b/doc/htmldoc/synapses/connection_management.rst @@ -113,7 +113,8 @@ Have a look at the section :ref:`handling_connections` to get more tips on how t Connection rules ---------------- -Here we elaborate on the connectivity concepts with details on :ref:`autapse_multapse`, :ref:`deterministic_rules`, :ref:`probabilistic_rules`, and finally the :ref:`conn_builder_conngen` (a method to create connections via CSA, the Connection Set Algebra [2]_). +Here we elaborate on the connectivity concepts with details on :ref:`autapse_multapse`, :ref:`deterministic_rules`, :ref:`probabilistic_rules`, and the :ref:`conn_builder_conngen` (a method to create connections via CSA, the Connection Set Algebra [2]_). +Finally, we introduce the rule :ref:`tripartite_bernoulli` for third-party connections in addition to primary connections between ``pre`` and ``post``. Each rule is described with an illustration, a NEST code example, and mathematical details. The mathematical details are extracted from the study on connectivity concepts [1]_ and contain a symbol which we recommend to use for describing this type of connectivity, the corresponding expression from CSA, and a formal definition with an algorithmic construction rule and the resulting connectivity distribution. @@ -486,6 +487,55 @@ As multapses are per default allowed and possible with this rule, you can disall | **Definition:** Each source node in :math:`S` is connected to :math:`K_\text{out}` nodes in :math:`\mathcal{T}` randomly chosen without replacement. | The out-degree distribution is by definition :math:`P(K)=\delta_{K,K_\text{out}}`, while the in-degree distribution is obtained by switching source and target indices, and replacing :math:`K_\text{out}` with :math:`K_\text{in}` in equation (2) from :ref:`fixed_indegree`. + +.. _conn_builder_conngen: + +Connection generator interface +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. admonition:: Availability + + This connection rule is only available if NEST was compiled with + :ref:`support for libneurosim `. + +To allow the generation of connectivity by means of an external +library, NEST supports the Connection Generator Interface [6]_. For +more details on this interface, see the git repository of `libneurosim +`_. + +In contrast to the other rules for creating connections, this rule +relies on a Connection Generator object to describe the connectivity +pattern in a library-specific way. The Connection Generator is handed +to :py:func:`.Connect` under the key ``cg`` of the connection specification +dictionary and evaluated internally. If the Connection Generator +provides values for connection weights and delays, their respective +indices can be specified under the key ``params_map``. Alternatively, +all synapse parameters can be specified using the synapse +specification argument to ``Connect()``. + +The following listing shows an example for using CSA (`Connection Set Algebra `_ [2]_) in NEST via the Connection +generator interface and randomly connects 10% of the neurons from +``A`` to the neurons in ``B``, each connection having a weight of +10000.0 pA and a delay of 1.0 ms: + +.. code-block:: python + + A = nest.Create('iaf_psc_alpha', 100) + B = nest.Create('iaf_psc_alpha', 100) + + # Create the Connection Generator object + import csa + cg = csa.cset(csa.random(0.1), 10000.0, 1.0) + + # Map weight and delay indices to values from cg + params_map = {'weight': 0, 'delay': 1} + + conn_spec = {'rule': 'conngen', 'cg': cg, 'params_map': params_map} + nest.Connect(A, B, conn_spec) + + +.. _tripartite_bernoulli: + Tripartite Bernoulli with pool ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -584,52 +634,6 @@ required because ``N_A/N_T`` = 2), and each node in ``A`` can be connected to one node in ``T``. -.. _conn_builder_conngen: - -Connection generator interface -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. admonition:: Availability - - This connection rule is only available if NEST was compiled with - :ref:`support for libneurosim `. - -To allow the generation of connectivity by means of an external -library, NEST supports the Connection Generator Interface [6]_. For -more details on this interface, see the git repository of `libneurosim -`_. - -In contrast to the other rules for creating connections, this rule -relies on a Connection Generator object to describe the connectivity -pattern in a library-specific way. The Connection Generator is handed -to :py:func:`.Connect` under the key ``cg`` of the connection specification -dictionary and evaluated internally. If the Connection Generator -provides values for connection weights and delays, their respective -indices can be specified under the key ``params_map``. Alternatively, -all synapse parameters can be specified using the synapse -specification argument to ``Connect()``. - -The following listing shows an example for using CSA (`Connection Set Algebra `_ [2]_) in NEST via the Connection -generator interface and randomly connects 10% of the neurons from -``A`` to the neurons in ``B``, each connection having a weight of -10000.0 pA and a delay of 1.0 ms: - -.. code-block:: python - - A = nest.Create('iaf_psc_alpha', 100) - B = nest.Create('iaf_psc_alpha', 100) - - # Create the Connection Generator object - import csa - cg = csa.cset(csa.random(0.1), 10000.0, 1.0) - - # Map weight and delay indices to values from cg - params_map = {'weight': 0, 'delay': 1} - - conn_spec = {'rule': 'conngen', 'cg': cg, 'params_map': params_map} - nest.Connect(A, B, conn_spec) - - References ---------- .. [1] Senk J, Kriener B, Djurfeldt M, Voges N, Jiang HJ, et al. (2022) Connectivity concepts in neuronal network modeling. PLOS Computational Biology 18(9): e1010086. https://doi.org/10.1371/journal.pcbi.1010086 From d5ab6fc694fbd97aabcb1936f4627f3cbc5d09a6 Mon Sep 17 00:00:00 2001 From: Johanna Senk Date: Tue, 5 Dec 2023 12:04:48 +0100 Subject: [PATCH 13/37] Reorder figure and text of tripartite examples, fix indentation in code --- .../synapses/connection_management.rst | 49 ++++++++++--------- 1 file changed, 25 insertions(+), 24 deletions(-) diff --git a/doc/htmldoc/synapses/connection_management.rst b/doc/htmldoc/synapses/connection_management.rst index a8087560b3..29cfafca26 100644 --- a/doc/htmldoc/synapses/connection_management.rst +++ b/doc/htmldoc/synapses/connection_management.rst @@ -568,9 +568,15 @@ second ``pool_size`` elements of ``A`` are the pool for the second node in ``T`` and so forth. In this case, ``N_T * pool_size == N_A`` is required. -The following code and figure demonstrate three use case examples with +The following figure and code demonstrate three use case examples with ``pool_type`` being ``'random'`` or ``'block'``: +.. image:: ../static/img/tripartite_pool_type.svg + :align: center + +(A) In the example of ``'random'`` pool type, each node in ``T`` can be connected with +up to two randomly selected nodes in ``A`` (given ``pool_size == 2``). + .. code-block:: python N_S, N_T, N_A, p_primary, p_third_if_primary = 6, 6, 3, 0.2, 1.0 @@ -586,6 +592,11 @@ The following code and figure demonstrate three use case examples with syn_specs = {'third_out': 'sic_connection'} nest.TripartiteConnect(S, T, A, conn_spec, syn_specs) +(B) In +the first example of ``'block'`` pool type, let ``N_T/N_A`` = 2, +then each node in ``T`` can be connected with one node in ``A`` +(``pool_size == 1`` is required because ``N_A < N_T``), and each node in +``A`` can be connected with up to two nodes in ``T``. .. code-block:: python @@ -595,13 +606,18 @@ The following code and figure demonstrate three use case examples with T = nest.Create('aeif_cond_alpha_astro', N_T) A = nest.Create('astrocyte_lr_1994', N_A) conn_spec = {'rule': 'tripartite_bernoulli_with_pool', - 'p_primary': p_primary, - 'p_third_if_primary': p_third_if_primary, - 'pool_type': pool_type, - 'pool_size': pool_size} + 'p_primary': p_primary, + 'p_third_if_primary': p_third_if_primary, + 'pool_type': pool_type, + 'pool_size': pool_size} syn_specs = {'third_out': 'sic_connection'} nest.TripartiteConnect(S, T, A, conn_spec, syn_specs) +(C) In the second example +of ``'block'`` pool type, let ``N_A/N_T`` = 2, then each node in +``T`` can be connected with up to two nodes in ``A`` (``pool_size == 2`` is +required because ``N_A/N_T`` = 2), and each node in ``A`` can be +connected to one node in ``T``. .. code-block:: python @@ -611,29 +627,14 @@ The following code and figure demonstrate three use case examples with T = nest.Create('aeif_cond_alpha_astro', N_T) A = nest.Create('astrocyte_lr_1994', N_A) conn_spec = {'rule': 'tripartite_bernoulli_with_pool', - 'p_primary': p_primary, - 'p_third_if_primary': p_third_if_primary, - 'pool_type': pool_type, - 'pool_size': pool_size} + 'p_primary': p_primary, + 'p_third_if_primary': p_third_if_primary, + 'pool_type': pool_type, + 'pool_size': pool_size} syn_specs = {'third_out': 'sic_connection'} nest.TripartiteConnect(S, T, A, conn_spec, syn_specs) -.. image:: ../static/img/tripartite_pool_type.svg - :align: center - -(A) In the example of ``'random'`` pool type, each node in ``T`` can be connected with -up to two randomly selected nodes in ``A`` (given ``pool_size == 2``). (B) In -the first example of ``'block'`` pool type, let ``N_T/N_A`` = 2, -then each node in ``T`` can be connected with one node in ``A`` -(``pool_size == 1`` is required because ``N_A < N_T``), and each node in -``A`` can be connected with up to two nodes in ``T``. (C) In the second example -of ``'block'`` pool type, let ``N_A/N_T`` = 2, then each node in -``T`` can be connected with up to two nodes in ``A`` (``pool_size == 2`` is -required because ``N_A/N_T`` = 2), and each node in ``A`` can be -connected to one node in ``T``. - - References ---------- .. [1] Senk J, Kriener B, Djurfeldt M, Voges N, Jiang HJ, et al. (2022) Connectivity concepts in neuronal network modeling. PLOS Computational Biology 18(9): e1010086. https://doi.org/10.1371/journal.pcbi.1010086 From 08a8ea08cb6388cdef31d0782274c428da3053f3 Mon Sep 17 00:00:00 2001 From: Johanna Senk Date: Tue, 5 Dec 2023 12:30:14 +0100 Subject: [PATCH 14/37] Suggestion to cite --- doc/htmldoc/synapses/connection_management.rst | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/doc/htmldoc/synapses/connection_management.rst b/doc/htmldoc/synapses/connection_management.rst index 29cfafca26..b6b83a7108 100644 --- a/doc/htmldoc/synapses/connection_management.rst +++ b/doc/htmldoc/synapses/connection_management.rst @@ -65,7 +65,10 @@ Connection management Basic connection rules commonly used in the computational neuroscience community. For more details, go to the section :ref:`conn_rules` or just click on one of the illustrations. -This documentation not only describes how to define network connectivity in NEST, but also represents a living reference for the connection rules defined in the article "Connectivity concepts in neuronal network modeling" [1]_. Deviations from the rules defined in the publication are highlighted in green. + +This documentation not only describes how to define network connectivity in NEST, but also provides details on connectivity concepts. +The article "Connectivity concepts in neuronal network modeling" [1]_ serves as a permanent reference for a number of connection rules and we suggest to cite it if rules defined there are used. +This documentation instead represents a living reference for these rules, and deviations from and extensions to what is described in the article are here highlighted in green. .. The same article also introduces a graphical notation for neuronal network diagrams which is curated in the documentation of NEST Desktop. TODO ADD LINK WHEN IT EXISTS From 9b2b4d60322461615221c0a2fb74acd99627c8ed Mon Sep 17 00:00:00 2001 From: Johanna Senk Date: Tue, 5 Dec 2023 12:34:14 +0100 Subject: [PATCH 15/37] Reword in handling_connections as suggested by @heplesser --- doc/htmldoc/synapses/handling_connections.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/htmldoc/synapses/handling_connections.rst b/doc/htmldoc/synapses/handling_connections.rst index 0a87b20df8..a6b4a0a88b 100644 --- a/doc/htmldoc/synapses/handling_connections.rst +++ b/doc/htmldoc/synapses/handling_connections.rst @@ -1,10 +1,10 @@ .. _handling_connections: -How to handle created connections -================================= +How to handle inspect and modify connections +============================================ Here, we describe how to inspect and modify already created connections. -For creating new connections, refer to :ref:`connection_management`, and for parameterizing synapses, see :ref:`synapse_spec`. +:ref:`connection_management` describes how to create new connections and :ref:`synapse_spec` how to parameterize synapses as they are created. Just like a NodeCollection is a container for node IDs, a SynapseCollection is a container for connections. In NEST 3, when you call :py:func:`.GetConnections` a From d892bc855cbad524c9e498b102aa068b443bc7c0 Mon Sep 17 00:00:00 2001 From: Johanna Senk Date: Tue, 5 Dec 2023 12:40:14 +0100 Subject: [PATCH 16/37] Add to whats new --- doc/htmldoc/synapses/connection_management.rst | 4 ++-- doc/htmldoc/whats_new/v3.7/index.rst | 8 ++++++++ 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/doc/htmldoc/synapses/connection_management.rst b/doc/htmldoc/synapses/connection_management.rst index b6b83a7108..b351bd0b24 100644 --- a/doc/htmldoc/synapses/connection_management.rst +++ b/doc/htmldoc/synapses/connection_management.rst @@ -117,7 +117,7 @@ Connection rules ---------------- Here we elaborate on the connectivity concepts with details on :ref:`autapse_multapse`, :ref:`deterministic_rules`, :ref:`probabilistic_rules`, and the :ref:`conn_builder_conngen` (a method to create connections via CSA, the Connection Set Algebra [2]_). -Finally, we introduce the rule :ref:`tripartite_bernoulli` for third-party connections in addition to primary connections between ``pre`` and ``post``. +Finally, we introduce the rule :ref:`tripartite_connectivity` for third-party connections in addition to primary connections between ``pre`` and ``post``. Each rule is described with an illustration, a NEST code example, and mathematical details. The mathematical details are extracted from the study on connectivity concepts [1]_ and contain a symbol which we recommend to use for describing this type of connectivity, the corresponding expression from CSA, and a formal definition with an algorithmic construction rule and the resulting connectivity distribution. @@ -537,7 +537,7 @@ generator interface and randomly connects 10% of the neurons from nest.Connect(A, B, conn_spec) -.. _tripartite_bernoulli: +.. _tripartite_connectivity: Tripartite Bernoulli with pool ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/doc/htmldoc/whats_new/v3.7/index.rst b/doc/htmldoc/whats_new/v3.7/index.rst index 4f4446d5dc..eeae9ad1df 100644 --- a/doc/htmldoc/whats_new/v3.7/index.rst +++ b/doc/htmldoc/whats_new/v3.7/index.rst @@ -40,3 +40,11 @@ See examples using astrocyte models: See connectivity documentation: * :ref:`tripartite_connectivity` + +Connectivity concepts +--------------------- +The documentation on :ref:`connection_management` now serves as a living reference for the connection rules defined in the article "Connectivity concepts in neuronal network modeling" [1]_. + +References +---------- +.. [1] Senk J, Kriener B, Djurfeldt M, Voges N, Jiang HJ, et al. (2022) Connectivity concepts in neuronal network modeling. PLOS Computational Biology 18(9): e1010086. https://doi.org/10.1371/journal.pcbi.1010086 From e57b8ee16f58e6b396ed543d7c5d6b58ccdc53b3 Mon Sep 17 00:00:00 2001 From: Johanna Senk Date: Tue, 5 Dec 2023 12:44:37 +0100 Subject: [PATCH 17/37] Minor --- doc/htmldoc/synapses/connection_management.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/htmldoc/synapses/connection_management.rst b/doc/htmldoc/synapses/connection_management.rst index b351bd0b24..1e10e4f10d 100644 --- a/doc/htmldoc/synapses/connection_management.rst +++ b/doc/htmldoc/synapses/connection_management.rst @@ -118,7 +118,7 @@ Connection rules Here we elaborate on the connectivity concepts with details on :ref:`autapse_multapse`, :ref:`deterministic_rules`, :ref:`probabilistic_rules`, and the :ref:`conn_builder_conngen` (a method to create connections via CSA, the Connection Set Algebra [2]_). Finally, we introduce the rule :ref:`tripartite_connectivity` for third-party connections in addition to primary connections between ``pre`` and ``post``. -Each rule is described with an illustration, a NEST code example, and mathematical details. +Each primary rule is described with an illustration, a NEST code example, and mathematical details. The mathematical details are extracted from the study on connectivity concepts [1]_ and contain a symbol which we recommend to use for describing this type of connectivity, the corresponding expression from CSA, and a formal definition with an algorithmic construction rule and the resulting connectivity distribution. .. dropdown:: Mathematical details: General notations and definitions From c8d9f6532890ceafa56e0f5106dac78415bad7ee Mon Sep 17 00:00:00 2001 From: Johanna Senk Date: Wed, 6 Dec 2023 11:28:58 +0100 Subject: [PATCH 18/37] Remove template, minor edits --- doc/htmldoc/synapses/connection_management.rst | 8 ++++---- doc/htmldoc/synapses/handling_connections.rst | 4 ++-- doc/htmldoc/templates/page.html | 14 -------------- 3 files changed, 6 insertions(+), 20 deletions(-) delete mode 100644 doc/htmldoc/templates/page.html diff --git a/doc/htmldoc/synapses/connection_management.rst b/doc/htmldoc/synapses/connection_management.rst index 1e10e4f10d..ded810e8b0 100644 --- a/doc/htmldoc/synapses/connection_management.rst +++ b/doc/htmldoc/synapses/connection_management.rst @@ -68,9 +68,9 @@ Connection management This documentation not only describes how to define network connectivity in NEST, but also provides details on connectivity concepts. The article "Connectivity concepts in neuronal network modeling" [1]_ serves as a permanent reference for a number of connection rules and we suggest to cite it if rules defined there are used. -This documentation instead represents a living reference for these rules, and deviations from and extensions to what is described in the article are here highlighted in green. +This documentation instead represents a living reference for these rules, and deviations from and extensions to what is described in the article will be highlighted. -.. The same article also introduces a graphical notation for neuronal network diagrams which is curated in the documentation of NEST Desktop. TODO ADD LINK WHEN IT EXISTS +.. The same article also introduces a graphical notation for neuronal network diagrams which will be curated in the documentation of :doc:`NEST Desktop `. :ref:`spatial_networks` are described on a separate page. @@ -494,7 +494,7 @@ As multapses are per default allowed and possible with this rule, you can disall .. _conn_builder_conngen: Connection generator interface -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +------------------------------ .. admonition:: Availability @@ -540,7 +540,7 @@ generator interface and randomly connects 10% of the neurons from .. _tripartite_connectivity: Tripartite Bernoulli with pool -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +------------------------------ For each possible pair of nodes from a source ``NodeCollection`` (e.g., a neuron population ``S``) and a target ``NodeCollection`` (e.g., a neuron population ``T``), a connection is diff --git a/doc/htmldoc/synapses/handling_connections.rst b/doc/htmldoc/synapses/handling_connections.rst index a6b4a0a88b..832670c8f6 100644 --- a/doc/htmldoc/synapses/handling_connections.rst +++ b/doc/htmldoc/synapses/handling_connections.rst @@ -1,7 +1,7 @@ .. _handling_connections: -How to handle inspect and modify connections -============================================ +How to inspect and modify connections +===================================== Here, we describe how to inspect and modify already created connections. :ref:`connection_management` describes how to create new connections and :ref:`synapse_spec` how to parameterize synapses as they are created. diff --git a/doc/htmldoc/templates/page.html b/doc/htmldoc/templates/page.html deleted file mode 100644 index b8163bddeb..0000000000 --- a/doc/htmldoc/templates/page.html +++ /dev/null @@ -1,14 +0,0 @@ -{% extends "!page.html" %} - -{% block footer %} - -{% endblock %} From 7482a5f3017294d0798e5f5350814fda54a2bd49 Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Wed, 6 Dec 2023 12:11:38 +0100 Subject: [PATCH 19/37] add class and directive to center text in rst --- doc/htmldoc/static/css/custom.css | 17 ++++++--- .../synapses/connection_management.rst | 37 +++++++++---------- 2 files changed, 28 insertions(+), 26 deletions(-) diff --git a/doc/htmldoc/static/css/custom.css b/doc/htmldoc/static/css/custom.css index fb519822d2..602832e9cb 100644 --- a/doc/htmldoc/static/css/custom.css +++ b/doc/htmldoc/static/css/custom.css @@ -1,4 +1,4 @@ -/* +/* * Customize css to define NEST specific colors, style and format * primary styles, for custom index.html and layout.html pages (see templates/ folder) @@ -8,7 +8,7 @@ Sphinx theme: sphinx-material https://bashtage.github.io/sphinx-material/index.html - Sphinx material theme properties use 'md' in variable names + Sphinx material theme properties use 'md' in variable names Extesion for grid / cards in reStructured Text see sphinx-design https://sphinx-design.readthedocs.io/ Sphinx design properties use the sd in variable names @@ -123,9 +123,14 @@ figure.align-default { outline-style: groove; } +.center { + + text-align: center; + font-style: italic; +} /************************************************************************************** - Settings for overriding material design theme + Settings for overriding material design theme Setting nest colors to override sphinx material "orange" and other default colors **************************************************************************************/ @@ -140,7 +145,7 @@ figure.align-default { font-weight: 600; } .md-typeset h1[id]:before, -.md-typeset h2[id]:before, +.md-typeset h2[id]:before, .md-typeset h3[id]:before, .md-typeset h4[id]:before { margin-top: -3.4rem; @@ -199,7 +204,7 @@ div[role="main"] .md-source { div.sd-card-title.sd-font-weight-bold.sd-d-flex-row.docutils a.reference.internal img { margin-right: 2em; margin-bottom: 0.5em; - + } .md-typeset .admonition, .md-typeset .admonition.tip { @@ -228,7 +233,7 @@ div.sd-card-title.sd-font-weight-bold.sd-d-flex-row.docutils a.reference.interna } .md-typeset .admonition.warning { border-left-color: #be1717; -} +} .md-typeset .admonition.warning > .admonition-title { background-color: #be1717; } diff --git a/doc/htmldoc/synapses/connection_management.rst b/doc/htmldoc/synapses/connection_management.rst index ded810e8b0..dd99b9242f 100644 --- a/doc/htmldoc/synapses/connection_management.rst +++ b/doc/htmldoc/synapses/connection_management.rst @@ -45,7 +45,7 @@ Connection management :link-type: ref .. image:: ../static/img/Fixed_total_number_H.png - + .. grid-item-card:: :link: fixed_indegree @@ -59,9 +59,7 @@ Connection management .. image:: ../static/img/Fixed_outdegree_H.png -.. card:: - :shadow: none - :text-align: center +.. rst-class:: center Basic connection rules commonly used in the computational neuroscience community. For more details, go to the section :ref:`conn_rules` or just click on one of the illustrations. @@ -178,7 +176,7 @@ nodes. | **Symbol:** :math:`\delta` | **CSA:** :math:`\delta` - | **Definition:** Each node in :math:`\mathcal{S}` is uniquely connected to one node in :math:`\mathcal{T}`. + | **Definition:** Each node in :math:`\mathcal{S}` is uniquely connected to one node in :math:`\mathcal{T}`. | :math:`\mathcal{S}` and :math:`\mathcal{T}` must have identical cardinality :math:`N_s=N_t`. Both sources and targets can be permuted independently even if :math:`\mathcal{S}=\mathcal{T}`. The in- and out-degree distributions are given by :math:`P(K)=\delta_{K,1}`, with Kronecker delta :math:`\delta_{i,j}=1` if :math:`i=j`, and zero otherwise. @@ -207,7 +205,7 @@ specified. | **Symbol:** :math:`\Omega` | **CSA:** :math:`\Omega` - | **Definition:** Each node in :math:`\mathcal{S}` is connected to all nodes in :math:`\mathcal{T}`. + | **Definition:** Each node in :math:`\mathcal{S}` is connected to all nodes in :math:`\mathcal{T}`. | The resulting edge set is the full edge set :math:`\mathcal{E}_\mathcal{ST}`. The in- and out-degree distributions are :math:`P_\text{in}(K)=\delta_{K,N_s}` for :math:`\mathcal{T}`, and :math:`P_\text{out}(K)=\delta_{K,N_t}` for :math:`\mathcal{S}`, respectively. Explicit connections @@ -227,7 +225,7 @@ Connections between explicit lists of source-target pairs can be realized in NES | **Symbol:** X | **CSA:** Not applicable - | **Definition:** Connections are established according to an explicit list of source-target pairs. + | **Definition:** Connections are established according to an explicit list of source-target pairs. | Connectivity is defined by an explicit list of sources and targets, also known as `adjacency list`, as for instance derived from anatomical measurements. It is, hence, not the result of any specific algorithm. An alternative way of representing a fixed connectivity is by means of the `adjacency matrix` :math:`A`, such that :math:`A_{ij}=1` if :math:`j` is connected to :math:`i`, and zero otherwise. We here adopt the common computational neuroscience practice to have the first index :math:`i` denote the target and the second index :math:`j` denote the source node. @@ -257,7 +255,7 @@ Note that multapses cannot be produced with this rule because each possible edge T = nest.Create('iaf_psc_alpha', m) conn_spec = {'rule': 'pairwise_bernoulli', 'p': p} nest.Connect(S, T, conn_spec) - + .. dropdown:: Mathematical details: Pairwise Bernoulli | **Symbol:** :math:`p` @@ -272,7 +270,7 @@ Note that multapses cannot be produced with this rule because each possible edge .. math:: P(K_\text{out}=K)=\mathcal{B}(K|N_t,p)\,, - + respectively. The expected total number of edges equals :math:`\text{E}[N_\text{syn}]=pN_tN_s`. @@ -325,7 +323,7 @@ As multapses are per default allowed and possible with this rule, you can disall | **CSA:** :math:`\mathbf{\rho_N}(N_\text{syn})\mathbf{M}(\mathbb{N}_S \times \mathbb{N}_T)` | **Definition:** :math:`N_\text{syn}\in\{0,\ldots,N_sN_t\}` edges are randomly drawn from the edge set :math:`\mathcal{E}_\mathcal{ST}` with replacement. | If multapses are allowed, there are :math:`\begin{pmatrix}N_sN_t+N_\text{syn}-1\\N_\text{syn}\end{pmatrix}` possible networks for any given number :math:`N_\text{syn}\leq N_sN_t`. - | Because exactly :math:`N_\text{syn}` connections are distributed across :math:`N_t` targets with replacement, the joint in-degree distribution is multinomial, + | Because exactly :math:`N_\text{syn}` connections are distributed across :math:`N_t` targets with replacement, the joint in-degree distribution is multinomial, .. math:: \begin{equation}\label{eq:randfixKm} @@ -336,7 +334,7 @@ As multapses are per default allowed and possible with this rule, you can disall \quad\quad 0 & \text{otherwise}\end{cases}\, \end{split} \end{equation} - + with :math:`p=1/N_t`. The out-degrees have an analogous multinomial distribution :math:`P(K_{\text{out},1}=K_1,\ldots,K_{\text{out},N_s}=K_{N_s})`, with :math:`p=1/N_s` and sources and targets switched. The marginal distributions are binomial distributions :math:`P(K_{\text{in},j}=K)= \mathcal{B}(K|N_\text{syn},1/N_t)` and :math:`P(K_{\text{out},j}=K)= \mathcal{B}(K|N_\text{syn},1/N_s)`, respectively. @@ -349,7 +347,7 @@ As multapses are per default allowed and possible with this rule, you can disall | **Symbol:** :math:`N_\text{syn} \cancel{M}` | **CSA:** :math:`\mathbf{\rho_{N}}(N_\text{syn})(\mathbb{N}_S \times \mathbb{N}_T)` - | **Definition:** :math:`N_\text{syn}\in\{0,\ldots,N_sN_t\}` edges are randomly drawn from the edge set :math:`\mathcal{E}_\mathcal{ST}` without replacement. + | **Definition:** :math:`N_\text{syn}\in\{0,\ldots,N_sN_t\}` edges are randomly drawn from the edge set :math:`\mathcal{E}_\mathcal{ST}` without replacement. | For :math:`\mathcal{S}=\mathcal{T}` this is a directed graph generalization of Erdős-Rényi graphs of the `constant number of edges` :math:`N_\text{syn}`-ensemble :math:`G(N,N_\text{syn})` [4]_. There are :math:`\begin{pmatrix}N_s N_t\\N_\text{syn}\end{pmatrix}` possible networks for any given number :math:`N_\text{syn}\leq N_sN_t`, which all have the same probability. The resulting in- and out-degree distributions are multivariate hypergeometric distributions. .. math:: @@ -413,19 +411,19 @@ As multapses are per default allowed and possible with this rule, you can disall \end{split} \end{equation} - The marginal distributions are binomial distributions + The marginal distributions are binomial distributions .. math:: \begin{equation}\label{eq:rfinmarg} P(K_{\text{out},j}=K)= \mathcal{B}(K|N_tK_\text{in},1/N_s)\,. \end{equation} - + .. dropdown:: Mathematical details: Random, fixed in-degree without multapses | **Symbol:** :math:`K_\text{in}, \cancel{M}` | **CSA:** :math:`{\rho_1}(K)(\mathbb{N}_S \times \mathbb{N}_T)` - | **Definition:** Each target node in :math:`\mathcal{T}` is connected to :math:`K_\text{in}` nodes in :math:`\mathcal{S}` randomly chosen without replacement. + | **Definition:** Each target node in :math:`\mathcal{T}` is connected to :math:`K_\text{in}` nodes in :math:`\mathcal{S}` randomly chosen without replacement. | The in-degree distribution is by definition :math:`P(K)=\delta_{K,K_\text{in}}`. To obtain the out-degree distribution, observe that after one target node has drawn its :math:`K_\text{out}` sources the joint probability distribution of out-degrees :math:`K_{\text{out},j}` is multivariate-hypergeometric such that .. math:: @@ -435,7 +433,7 @@ As multapses are per default allowed and possible with this rule, you can disall & \quad \quad \quad= \begin{cases} \prod_{j=1}^{N_s} \begin{pmatrix} 1\\K_j\end{pmatrix}\Bigg/\begin{pmatrix} N_s\\K_\text{in}\end{pmatrix} & \text{if}\,\,\sum_{j=1}^{N_s} K_j = K_\text{in}\\ - \phantom{bl}0 & \text{otherwise}\end{cases}\,, \qquad (1) + \phantom{bl}0 & \text{otherwise}\end{cases}\,, \qquad (1) \end{split} \end{equation} @@ -447,7 +445,7 @@ As multapses are per default allowed and possible with this rule, you can disall P(K_{\text{out},j}=K)= \begin{pmatrix} 1\\K \end{pmatrix} \begin{pmatrix}N_s-1 \\ K_\text{in}-K \end{pmatrix}\Bigg/\begin{pmatrix}N_s - \\ K_\text{in}\end{pmatrix} = \text{Ber}(K_\text{in}/N_s)\,, \qquad (2) + \\ K_\text{in}\end{pmatrix} = \text{Ber}(K_\text{in}/N_s)\,, \qquad (2) \end{eqnarray} with :math:`\text{Ber}(p)` denoting the Bernoulli distribution with parameter :math:`p`, because :math:`K\in\{0,1\}`. @@ -480,14 +478,14 @@ As multapses are per default allowed and possible with this rule, you can disall | **Symbol:** :math:`K_\text{out}, M` | **CSA:** :math:`\mathbf{\rho_0}(K)\mathbf{M}(\mathbb{N}_S \times \mathbb{N}_T)` - | **Definition:** Each source node in :math:`\mathcal{S}` is connected to :math:`K_\text{out}` nodes in :math:`\mathcal{T}` randomly chosen with replacement. + | **Definition:** Each source node in :math:`\mathcal{S}` is connected to :math:`K_\text{out}` nodes in :math:`\mathcal{T}` randomly chosen with replacement. | By definition, the out-degree distribution is a :math:`P(K)=\delta_{K,K_\text{out}}`. The respective in-degree distribution and marginal distributions are obtained by switching source and target indices, and replacing :math:`K_\text{out}` with :math:`K_\text{in}` in equation from :ref:`fixed_indegree` [5]_. .. dropdown:: Mathematical details: Random, fixed out-degree without multapses | **Symbol:** :math:`K_\text{out},\cancel{M}` | **CSA:** :math:`\mathbf{\rho_0}(K)(\mathbb{N}_S \times \mathbb{N}_T)` - | **Definition:** Each source node in :math:`S` is connected to :math:`K_\text{out}` nodes in :math:`\mathcal{T}` randomly chosen without replacement. + | **Definition:** Each source node in :math:`S` is connected to :math:`K_\text{out}` nodes in :math:`\mathcal{T}` randomly chosen without replacement. | The out-degree distribution is by definition :math:`P(K)=\delta_{K,K_\text{out}}`, while the in-degree distribution is obtained by switching source and target indices, and replacing :math:`K_\text{out}` with :math:`K_\text{in}` in equation (2) from :ref:`fixed_indegree`. @@ -649,4 +647,3 @@ References connectivity in neuronal networks from simulator-independent descriptions. Front. Neuroinform. https://doi.org/10.3389/fninf.2014.00043 - From 8c51aab1bc31e2151306406c118989034a42eac0 Mon Sep 17 00:00:00 2001 From: clemensk0 Date: Wed, 6 Dec 2023 16:18:35 +0100 Subject: [PATCH 20/37] add links and change overview --- doc/htmldoc/synapses/connection_management.rst | 7 +------ doc/htmldoc/synapses/synapse_specification.rst | 8 ++++---- 2 files changed, 5 insertions(+), 10 deletions(-) diff --git a/doc/htmldoc/synapses/connection_management.rst b/doc/htmldoc/synapses/connection_management.rst index dd99b9242f..2b5f849f8f 100644 --- a/doc/htmldoc/synapses/connection_management.rst +++ b/doc/htmldoc/synapses/connection_management.rst @@ -3,7 +3,7 @@ Connection management ===================== -.. grid:: 4 +.. grid:: 8 :gutter: 1 .. grid-item-card:: @@ -30,23 +30,18 @@ Connection management .. image:: ../static/img/All_to_all_H.png -.. grid:: 4 - :gutter: 1 - .. grid-item-card:: :link: pairwise_bernoulli :link-type: ref .. image:: ../static/img/Pairwise_bernoulli_H.png - .. grid-item-card:: :link: fixed_total_number :link-type: ref .. image:: ../static/img/Fixed_total_number_H.png - .. grid-item-card:: :link: fixed_indegree :link-type: ref diff --git a/doc/htmldoc/synapses/synapse_specification.rst b/doc/htmldoc/synapses/synapse_specification.rst index 649f835fa1..c869708601 100644 --- a/doc/htmldoc/synapses/synapse_specification.rst +++ b/doc/htmldoc/synapses/synapse_specification.rst @@ -72,7 +72,7 @@ specified as arrays of the correct type. One-to-one ^^^^^^^^^^ -For rule ``one_to_one`` the array must have the same length as there +For rule :ref:`one_to_one` the array must have the same length as there are nodes in ``A`` and ``B``. .. code-block:: python @@ -86,7 +86,7 @@ are nodes in ``A`` and ``B``. All-to-all ^^^^^^^^^^ -When connecting with rule ``all_to_all``, the array parameter must +When connecting with rule :ref:`all_to_all`, the array parameter must have dimension `len(B) x len(A)`. .. code-block:: python @@ -99,7 +99,7 @@ have dimension `len(B) x len(A)`. Random, fixed total number ^^^^^^^^^^^^^^^^^^^^^^^^^^ -For rule ``fixed_total_number``, the array has to be same the length as the +For rule :ref:`fixed_total_number`, the array has to be same the length as the number of connections ``N``. .. code-block:: python @@ -113,7 +113,7 @@ number of connections ``N``. Random, fixed in-degree ^^^^^^^^^^^^^^^^^^^^^^^ -For rule ``fixed_indegree`` the array has to be a two-dimensional +For rule :ref:`fixed_indegree` the array has to be a two-dimensional NumPy array or Python list with shape ``(len(B), indegree)``, where :hxt_ref:`indegree` is the number of incoming connections per target neuron. This means that the rows describe the target, while the columns From e390aafea3bd0f3e760ef1c4eb42ca109662d08c Mon Sep 17 00:00:00 2001 From: clemensk0 Date: Wed, 6 Dec 2023 18:14:38 +0100 Subject: [PATCH 21/37] change heading and restructuring --- doc/htmldoc/synapses/connection_generator.rst | 53 +++++++++++ ...anagement.rst => connectivity_concept.rst} | 90 +++++-------------- doc/htmldoc/synapses/handling_connections.rst | 6 +- .../synapses/synapse_specification.rst | 7 +- doc/htmldoc/understand_index.rst | 4 +- 5 files changed, 83 insertions(+), 77 deletions(-) create mode 100644 doc/htmldoc/synapses/connection_generator.rst rename doc/htmldoc/synapses/{connection_management.rst => connectivity_concept.rst} (91%) diff --git a/doc/htmldoc/synapses/connection_generator.rst b/doc/htmldoc/synapses/connection_generator.rst new file mode 100644 index 0000000000..77e3e9eda3 --- /dev/null +++ b/doc/htmldoc/synapses/connection_generator.rst @@ -0,0 +1,53 @@ +.. _connection_generator: + +Connection generator interface +------------------------------ + +.. admonition:: Availability + + This connection rule is only available if NEST was compiled with + :ref:`support for libneurosim `. + +To allow the generation of connectivity by means of an external +library, NEST supports the Connection Generator Interface [2]_. For +more details on this interface, see the git repository of `libneurosim +`_. + +In contrast to the other rules for creating connections, this rule +relies on a Connection Generator object to describe the connectivity +pattern in a library-specific way. The Connection Generator is handed +to :py:func:`.Connect` under the key ``cg`` of the connection specification +dictionary and evaluated internally. If the Connection Generator +provides values for connection weights and delays, their respective +indices can be specified under the key ``params_map``. Alternatively, +all synapse parameters can be specified using the synapse +specification argument to ``Connect()``. + +The following listing shows an example for using CSA (`Connection Set Algebra `_ [1]_) in NEST via the Connection +generator interface and randomly connects 10% of the neurons from +``A`` to the neurons in ``B``, each connection having a weight of +10000.0 pA and a delay of 1.0 ms: + +.. code-block:: python + + A = nest.Create('iaf_psc_alpha', 100) + B = nest.Create('iaf_psc_alpha', 100) + + # Create the Connection Generator object + import csa + cg = csa.cset(csa.random(0.1), 10000.0, 1.0) + + # Map weight and delay indices to values from cg + params_map = {'weight': 0, 'delay': 1} + + conn_spec = {'rule': 'conngen', 'cg': cg, 'params_map': params_map} + nest.Connect(A, B, conn_spec) + + +References +---------- +.. [1] Djurfeldt M. The Connection-set Algebra—A Novel Formalism for the Representation of Connectivity Structure in Neuronal Network Models. Neuroinformatics. 2012; 10: 287–304. https://doi.org/10.1007/s12021-012-9146-1 +.. [2] Djurfeldt M, Davison AP and Eppler JM (2014). Efficient generation of + connectivity in neuronal networks from simulator-independent + descriptions. Front. Neuroinform. + https://doi.org/10.3389/fninf.2014.00043 diff --git a/doc/htmldoc/synapses/connection_management.rst b/doc/htmldoc/synapses/connectivity_concept.rst similarity index 91% rename from doc/htmldoc/synapses/connection_management.rst rename to doc/htmldoc/synapses/connectivity_concept.rst index 2b5f849f8f..8eb25d92d1 100644 --- a/doc/htmldoc/synapses/connection_management.rst +++ b/doc/htmldoc/synapses/connectivity_concept.rst @@ -1,58 +1,58 @@ -.. _connection_management: +.. _connectivity_concept: -Connection management +Connectivity concept ===================== .. grid:: 8 - :gutter: 1 + :gutter: 0 - .. grid-item-card:: + .. grid-item-card:: Autapse :link: autapse_multapse :link-type: ref - .. image:: ../static/img/Autapse_H.png + .. image:: ../static/img/Autapse.png - .. grid-item-card:: + .. grid-item-card:: Multapse :link: autapse_multapse :link-type: ref + + .. image:: ../static/img/Multapse.png - .. image:: ../static/img/Multapse_H.png - - .. grid-item-card:: + .. grid-item-card:: One to one :link: one_to_one :link-type: ref - .. image:: ../static/img/One_to_one_H.png + .. image:: ../static/img/One_to_one.png - .. grid-item-card:: + .. grid-item-card:: All to all :link: all_to_all :link-type: ref - .. image:: ../static/img/All_to_all_H.png + .. image:: ../static/img/All_to_all.png - .. grid-item-card:: + .. grid-item-card:: Pairwise Bernoulli :link: pairwise_bernoulli :link-type: ref - .. image:: ../static/img/Pairwise_bernoulli_H.png + .. image:: ../static/img/Pairwise_bernoulli.png - .. grid-item-card:: + .. grid-item-card:: Fixed total number :link: fixed_total_number :link-type: ref - .. image:: ../static/img/Fixed_total_number_H.png + .. image:: ../static/img/Fixed_total_number.png - .. grid-item-card:: + .. grid-item-card:: Fixed in-degree :link: fixed_indegree :link-type: ref - .. image:: ../static/img/Fixed_indegree_H.png + .. image:: ../static/img/Fixed_indegree.png - .. grid-item-card:: + .. grid-item-card:: Fixed out-degree :link: fixed_outdegree :link-type: ref - .. image:: ../static/img/Fixed_outdegree_H.png + .. image:: ../static/img/Fixed_outdegree.png .. rst-class:: center @@ -484,52 +484,6 @@ As multapses are per default allowed and possible with this rule, you can disall | The out-degree distribution is by definition :math:`P(K)=\delta_{K,K_\text{out}}`, while the in-degree distribution is obtained by switching source and target indices, and replacing :math:`K_\text{out}` with :math:`K_\text{in}` in equation (2) from :ref:`fixed_indegree`. -.. _conn_builder_conngen: - -Connection generator interface ------------------------------- - -.. admonition:: Availability - - This connection rule is only available if NEST was compiled with - :ref:`support for libneurosim `. - -To allow the generation of connectivity by means of an external -library, NEST supports the Connection Generator Interface [6]_. For -more details on this interface, see the git repository of `libneurosim -`_. - -In contrast to the other rules for creating connections, this rule -relies on a Connection Generator object to describe the connectivity -pattern in a library-specific way. The Connection Generator is handed -to :py:func:`.Connect` under the key ``cg`` of the connection specification -dictionary and evaluated internally. If the Connection Generator -provides values for connection weights and delays, their respective -indices can be specified under the key ``params_map``. Alternatively, -all synapse parameters can be specified using the synapse -specification argument to ``Connect()``. - -The following listing shows an example for using CSA (`Connection Set Algebra `_ [2]_) in NEST via the Connection -generator interface and randomly connects 10% of the neurons from -``A`` to the neurons in ``B``, each connection having a weight of -10000.0 pA and a delay of 1.0 ms: - -.. code-block:: python - - A = nest.Create('iaf_psc_alpha', 100) - B = nest.Create('iaf_psc_alpha', 100) - - # Create the Connection Generator object - import csa - cg = csa.cset(csa.random(0.1), 10000.0, 1.0) - - # Map weight and delay indices to values from cg - params_map = {'weight': 0, 'delay': 1} - - conn_spec = {'rule': 'conngen', 'cg': cg, 'params_map': params_map} - nest.Connect(A, B, conn_spec) - - .. _tripartite_connectivity: Tripartite Bernoulli with pool @@ -638,7 +592,3 @@ References .. [3] Albert R, Barabási AL. Statistical mechanics of complex networks. Rev Mod Phys. 2002; 74: 47–97. https://doi.org/10.1103/RevModPhys.74.47 .. [4] Erdős P, Rényi A. On random graphs. Publications Mathematicae. 1959; 6: 290–297. .. [5] Hjertholm D. Statistical tests for connection algorithms for structured neural networks [master’s thesis]. Norwegian University of Life Sciences. Ås, Norway; 2013. Available from: http://hdl.handle.net/11250/189117 -.. [6] Djurfeldt M, Davison AP and Eppler JM (2014). Efficient generation of - connectivity in neuronal networks from simulator-independent - descriptions. Front. Neuroinform. - https://doi.org/10.3389/fninf.2014.00043 diff --git a/doc/htmldoc/synapses/handling_connections.rst b/doc/htmldoc/synapses/handling_connections.rst index 832670c8f6..123d2e59e5 100644 --- a/doc/htmldoc/synapses/handling_connections.rst +++ b/doc/htmldoc/synapses/handling_connections.rst @@ -1,10 +1,10 @@ .. _handling_connections: -How to inspect and modify connections -===================================== +Working with connections +========================= Here, we describe how to inspect and modify already created connections. -:ref:`connection_management` describes how to create new connections and :ref:`synapse_spec` how to parameterize synapses as they are created. +:ref:`connectivity_concept` describes how to create new connections, :ref:`connection_generator` for generation of connectivity and :ref:`synapse_spec` how to parameterize synapses as they are created. Just like a NodeCollection is a container for node IDs, a SynapseCollection is a container for connections. In NEST 3, when you call :py:func:`.GetConnections` a diff --git a/doc/htmldoc/synapses/synapse_specification.rst b/doc/htmldoc/synapses/synapse_specification.rst index c869708601..0339a95fec 100644 --- a/doc/htmldoc/synapses/synapse_specification.rst +++ b/doc/htmldoc/synapses/synapse_specification.rst @@ -3,7 +3,8 @@ Synapse specification ======================== -This page is about parameterizing synapses. See :ref:`connection_management` for specifying connections, and :ref:`handling_connections` for inspecting and modifying ``SynapseCollections``. +This page is about parameterizing synapses. See :ref:`connectivity_concept` for specifying connections, :ref:`connection_generator` for generation of connectivity and :ref:`handling_connections` for inspecting and modifying ``SynapseCollections``. + The synapse properties can be given as just the name of the desired synapse model as a string, as a dictionary specifying synapse model @@ -64,7 +65,7 @@ Array parameters Array parameters can be used with the rules ``one_to_one``, ``all_to_all``, ``fixed_total_number``, ``fixed_indegree``, and ``fixed_outdegree``. -For details on connection rules, see :ref:`connection_management`. +For details on connection rules, see :ref:`connectivity_concept`. The arrays can be specified as NumPy arrays or Python lists. As with the scalar parameters, all parameters have to be specified as arrays of the correct type. @@ -131,7 +132,7 @@ of the identity of the source neurons. Random, fixed out-degree ^^^^^^^^^^^^^^^^^^^^^^^^ -For rule ``fixed_outdegree`` the array has to be a two-dimensional +For rule :ref:`fixed_outdegree` the array has to be a two-dimensional NumPy array or Python list with shape ``(len(A), outdegree)``, where :hxt_ref:`outdegree` is the number of outgoing connections per source neuron. This means that the rows describe the source, while the diff --git a/doc/htmldoc/understand_index.rst b/doc/htmldoc/understand_index.rst index 729d56d338..57d4339b54 100644 --- a/doc/htmldoc/understand_index.rst +++ b/doc/htmldoc/understand_index.rst @@ -19,10 +19,12 @@ Here you can dive into various topics about NEST. .. grid-item-card:: |synapse| All about synapses and connections :class-title: sd-d-flex-row sd-align-minor-center - * :ref:`connection_management` + * :ref:`connectivity_concept` * :ref:`synapse_spec` + * :ref:`connection_generator` + * :ref:`handling_connections` * :ref:`sim_gap_junctions` From 93cce46adf98e0ccc451b85129dea082aa21e0a6 Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Thu, 7 Dec 2023 12:26:00 +0100 Subject: [PATCH 22/37] modify top grid images --- doc/htmldoc/synapses/connectivity_concept.rst | 56 ++++++++----------- 1 file changed, 24 insertions(+), 32 deletions(-) diff --git a/doc/htmldoc/synapses/connectivity_concept.rst b/doc/htmldoc/synapses/connectivity_concept.rst index 8eb25d92d1..dec7e671ac 100644 --- a/doc/htmldoc/synapses/connectivity_concept.rst +++ b/doc/htmldoc/synapses/connectivity_concept.rst @@ -3,56 +3,48 @@ Connectivity concept ===================== -.. grid:: 8 +.. grid:: 2 2 7 7 :gutter: 0 - .. grid-item-card:: Autapse - :link: autapse_multapse - :link-type: ref + .. grid-item-card:: Autapse and multapse + :link: autapse_multapse + :link-type: ref + :img-top: ../static/img/Autapse_multapse_v.png - .. image:: ../static/img/Autapse.png - - .. grid-item-card:: Multapse - :link: autapse_multapse - :link-type: ref - - .. image:: ../static/img/Multapse.png .. grid-item-card:: One to one - :link: one_to_one - :link-type: ref + :link: one_to_one + :link-type: ref + :img-top: ../static/img/One_to_one.png - .. image:: ../static/img/One_to_one.png .. grid-item-card:: All to all - :link: all_to_all - :link-type: ref - - .. image:: ../static/img/All_to_all.png + :link: all_to_all + :link-type: ref + :img-top: ../static/img/All_to_all.png .. grid-item-card:: Pairwise Bernoulli - :link: pairwise_bernoulli - :link-type: ref + :link: pairwise_bernoulli + :link-type: ref + :img-top: ../static/img/Pairwise_bernoulli.png - .. image:: ../static/img/Pairwise_bernoulli.png .. grid-item-card:: Fixed total number - :link: fixed_total_number - :link-type: ref + :link: fixed_total_number + :link-type: ref + :img-top: ../static/img/Fixed_total_number.png - .. image:: ../static/img/Fixed_total_number.png .. grid-item-card:: Fixed in-degree - :link: fixed_indegree - :link-type: ref + :link: fixed_indegree + :link-type: ref + :img-top: ../static/img/Fixed_indegree.png - .. image:: ../static/img/Fixed_indegree.png .. grid-item-card:: Fixed out-degree - :link: fixed_outdegree - :link-type: ref - - .. image:: ../static/img/Fixed_outdegree.png + :link: fixed_outdegree + :link-type: ref + :img-top: ../static/img/Fixed_outdegree.png .. rst-class:: center @@ -109,7 +101,7 @@ Have a look at the section :ref:`handling_connections` to get more tips on how t Connection rules ---------------- -Here we elaborate on the connectivity concepts with details on :ref:`autapse_multapse`, :ref:`deterministic_rules`, :ref:`probabilistic_rules`, and the :ref:`conn_builder_conngen` (a method to create connections via CSA, the Connection Set Algebra [2]_). +Here we elaborate on the connectivity concepts with details on :ref:`autapse_multapse`, :ref:`deterministic_rules`, :ref:`probabilistic_rules`, and the :ref:`connection_generator` (a method to create connections via CSA, the Connection Set Algebra [2]_). Finally, we introduce the rule :ref:`tripartite_connectivity` for third-party connections in addition to primary connections between ``pre`` and ``post``. Each primary rule is described with an illustration, a NEST code example, and mathematical details. The mathematical details are extracted from the study on connectivity concepts [1]_ and contain a symbol which we recommend to use for describing this type of connectivity, the corresponding expression from CSA, and a formal definition with an algorithmic construction rule and the resulting connectivity distribution. 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zRd`v79P|3q4G@Huv?~B9OeF{F>33h@kdqxcDu@i81bNZ8d1#~Va6*dH=vb8&(^8Yw zmi@e|if`DfT7@r$S5Zdyk5eHk*XLKdmjU3|pi0b*rq11I9Q+ZE!`MErj&=TYnc+X+ zrZe*fgz}pKg~q#6f&t{dbI)9y-JNiU;O$f-v=;KqRNm_Q$J?Xdo^;SPI5~VL@ezJ* z2+R0!LtqlVX^f;omgwcEiBp6=oZj{cYdkT|lvePbq1RD!xHNC6Q)b zFaXnjkYxW4<4Ah@%MYD@xl&)yz-#ntg2Im#h=h)>#ML>;e1)9-v%#7-y~A^(iQ0TstTl!5k!mHcbQRYmYz$LB>U|zfZ23wBcDKILwtzkJ^48k| zLpG`UyXgd3x1f^ztBQ%;hE}HUx_+FpMWtG8OV=b=MRwuOA46QG;M3N)fx{gTDeV!6 zMtzNoSSr$gk{ED2qKO#Wawm$M)N>|zPA^99B4c;#$q;hLVba@fhfx&x?GbQHM9>ak z&9UC~#y9D54h+8X!ZMh=7|MNdc$)NA?UxS{6&-({Q$I7I^Lh?i-p1L7dNI8GZARQU z>O^0Q+3GxkKwxs)`e@Clp*gNO?f|;oS^)NeKrpC5-#P?;9B_6K+WB1SH!}(WvOehr zuR!fdCViMpqO8oSo8}0TUUL4Q3;Cfp9wI{) z=l`F+u(R94q+J1=1!~eP+}wuf1EpZ7)jB{hIJaYb;)NOv(pFXRh8}bcj)bkV8%}0h zJ_(92%m2%K{y&d8f7lLZK;uOV_xEgv*UZ2iA0E?bF%8_hbs7&JB2<-bDVE5azW8q| CV=Ko1 From 6259a9c224a82e8d8acb9c6c9b263e420531ba69 Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Thu, 7 Dec 2023 12:27:28 +0100 Subject: [PATCH 24/37] update links to new label names --- doc/htmldoc/installation/cmake_options.rst | 2 +- .../nest_behavior/running_simulations.rst | 2 +- .../spatially_structured_networks.rst | 2 +- .../synapses/synapse_specification.rst | 21 +++++++++---------- ...t_1_neurons_and_simple_neural_networks.rst | 2 +- .../part_2_populations_of_neurons.rst | 4 ++-- ...rt_3_connecting_networks_with_synapses.rst | 2 +- doc/htmldoc/whats_new/v3.7/index.rst | 3 ++- models/multimeter.h | 4 ++-- 9 files changed, 21 insertions(+), 21 deletions(-) diff --git a/doc/htmldoc/installation/cmake_options.rst b/doc/htmldoc/installation/cmake_options.rst index 59123e9e61..ad8090062c 100644 --- a/doc/htmldoc/installation/cmake_options.rst +++ b/doc/htmldoc/installation/cmake_options.rst @@ -262,7 +262,7 @@ follwing switch for the invocation of ``cmake``. It expects either -Dwith-libneurosim=[OFF|ON|] For details on how to use the Connection Generator Interface, see the -:ref:`guide on connection management `. +:ref:`guide on connection generation `. .. _compile_with_python: diff --git a/doc/htmldoc/nest_behavior/running_simulations.rst b/doc/htmldoc/nest_behavior/running_simulations.rst index 069f61b65c..07c250c787 100644 --- a/doc/htmldoc/nest_behavior/running_simulations.rst +++ b/doc/htmldoc/nest_behavior/running_simulations.rst @@ -129,7 +129,7 @@ NEST also has some models that determine the precise time of the threshold crossing during the interval. Please see the documentation on :ref:`precise spike time neurons ` for details about neuron update in continuous time and the -:ref:`documentation on connection management ` +:ref:`documentation on the connectivty concept ` for how to set the delay when creating synapses. .. _stepped_simulations: diff --git a/doc/htmldoc/networks/spatially_structured_networks.rst b/doc/htmldoc/networks/spatially_structured_networks.rst index 2148e063c5..fed8d7adb7 100644 --- a/doc/htmldoc/networks/spatially_structured_networks.rst +++ b/doc/htmldoc/networks/spatially_structured_networks.rst @@ -1317,7 +1317,7 @@ The following table presents some query functions provided by NEST. +---------------------------------+-----------------------------------------------------+ | ``nest.GetConnections()`` | Retrieve connections (all or for a given | | | source or target); see also | -| | http://www.nest-simulator.org/connection_management.| +| | :ref:`connectivity_concept`. | +---------------------------------+-----------------------------------------------------+ | ``nest.GetNodes()`` | Returns a NodeCollection of all elements with given | | | properties. | diff --git a/doc/htmldoc/synapses/synapse_specification.rst b/doc/htmldoc/synapses/synapse_specification.rst index 0339a95fec..1471c78e5f 100644 --- a/doc/htmldoc/synapses/synapse_specification.rst +++ b/doc/htmldoc/synapses/synapse_specification.rst @@ -1,7 +1,7 @@ .. _synapse_spec: Synapse specification -======================== +===================== This page is about parameterizing synapses. See :ref:`connectivity_concept` for specifying connections, :ref:`connection_generator` for generation of connectivity and :ref:`handling_connections` for inspecting and modifying ``SynapseCollections``. @@ -41,7 +41,7 @@ as long as they are supported by the chosen connection rule. .. _scalar_params: Scalar parameters -~~~~~~~~~~~~~~~~~ +----------------- Scalar parameters must be given with the correct type. The ``weight`` for instance must be a float, while the ``receptor_type`` has to be of @@ -61,7 +61,7 @@ will be applied to all connections created in the current .. _array_params: Array parameters -~~~~~~~~~~~~~~~~ +---------------- Array parameters can be used with the rules ``one_to_one``, ``all_to_all``, ``fixed_total_number``, ``fixed_indegree``, and ``fixed_outdegree``. @@ -71,7 +71,7 @@ lists. As with the scalar parameters, all parameters have to be specified as arrays of the correct type. One-to-one -^^^^^^^^^^ +~~~~~~~~~~ For rule :ref:`one_to_one` the array must have the same length as there are nodes in ``A`` and ``B``. @@ -85,7 +85,7 @@ are nodes in ``A`` and ``B``. nest.Connect(A, B, conn_spec_dict, syn_spec_dict) All-to-all -^^^^^^^^^^ +~~~~~~~~~~ When connecting with rule :ref:`all_to_all`, the array parameter must have dimension `len(B) x len(A)`. @@ -98,7 +98,7 @@ have dimension `len(B) x len(A)`. nest.Connect(A, B, syn_spec=syn_spec_dict) Random, fixed total number -^^^^^^^^^^^^^^^^^^^^^^^^^^ +~~~~~~~~~~~~~~~~~~~~~~~~~~ For rule :ref:`fixed_total_number`, the array has to be same the length as the number of connections ``N``. @@ -112,7 +112,7 @@ number of connections ``N``. nest.Connect(A, B, conn_spec_dict, syn_spec_dict) Random, fixed in-degree -^^^^^^^^^^^^^^^^^^^^^^^ +~~~~~~~~~~~~~~~~~~~~~~~ For rule :ref:`fixed_indegree` the array has to be a two-dimensional NumPy array or Python list with shape ``(len(B), indegree)``, where @@ -130,7 +130,7 @@ of the identity of the source neurons. nest.Connect(A, B, conn_spec_dict, syn_spec_dict) Random, fixed out-degree -^^^^^^^^^^^^^^^^^^^^^^^^ +~~~~~~~~~~~~~~~~~~~~~~~~ For rule :ref:`fixed_outdegree` the array has to be a two-dimensional NumPy array or Python list with shape ``(len(A), outdegree)``, where @@ -150,7 +150,7 @@ regardless of the identity of the target neuron. .. _dist_params: Expressions as parameters -~~~~~~~~~~~~~~~~~~~~~~~~~ +------------------------- ``nest.Parameter`` objects support a flexible specification of synapse parameters through expressions. This includes parameters drawn from random @@ -215,7 +215,7 @@ For further information on the available distributions see .. _collocated_synapses: Collocated synapses -~~~~~~~~~~~~~~~~~~~ +------------------- Some modeling applications require multiple connections between the same pairs of nodes. An example of this could be a network, where each @@ -716,4 +716,3 @@ using the dot-notation: Note that some parameters like ``source`` and ``target`` are read-only and cannot be set. The documentation of a specific synapse model will point out which parameters can be set and which are read-only. - diff --git a/doc/htmldoc/tutorials/pynest_tutorial/part_1_neurons_and_simple_neural_networks.rst b/doc/htmldoc/tutorials/pynest_tutorial/part_1_neurons_and_simple_neural_networks.rst index d810e77c76..380845275d 100644 --- a/doc/htmldoc/tutorials/pynest_tutorial/part_1_neurons_and_simple_neural_networks.rst +++ b/doc/htmldoc/tutorials/pynest_tutorial/part_1_neurons_and_simple_neural_networks.rst @@ -510,7 +510,7 @@ Connections This is an abbreviated version of the documentation for the :py:func:`.Connect` function, please see NEST's online help for the full version and -:ref:`Connection Management ` for an introduction +:ref:`Connectivity concept ` for an introduction and examples. - ``Connect(pre, post, conn_spec=None, syn_spec=None, return_synapsecollection=False)`` diff --git a/doc/htmldoc/tutorials/pynest_tutorial/part_2_populations_of_neurons.rst b/doc/htmldoc/tutorials/pynest_tutorial/part_2_populations_of_neurons.rst index 6fc2395032..b1a8dbe662 100644 --- a/doc/htmldoc/tutorials/pynest_tutorial/part_2_populations_of_neurons.rst +++ b/doc/htmldoc/tutorials/pynest_tutorial/part_2_populations_of_neurons.rst @@ -202,7 +202,7 @@ Here we have just used very simple connection schemes. Connectivity patterns requiring the specification of further parameters, such as in-degree or connection probabilities, must be defined in a dictionary containing the key ``rule`` and the key for parameters associated to the -rule. Please see :ref:`Connection management ` +rule. Please see :ref:`Connectivity concept ` for an illustrated guide to the usage of :py:func:`.Connect`, as well as the example below. Connecting populations with random connections @@ -268,7 +268,7 @@ the same population simultaneously in the role of ``pre`` and ``post``. For more information on connecting neurons, please read the documentation of the :py:func:`.Connect` function and consult the guide at -:ref:`Connection management `. +:ref:`connectivity concept `. Specifying the behaviour of devices ----------------------------------- diff --git a/doc/htmldoc/tutorials/pynest_tutorial/part_3_connecting_networks_with_synapses.rst b/doc/htmldoc/tutorials/pynest_tutorial/part_3_connecting_networks_with_synapses.rst index 93a2b2ea8d..2ccef0b9a2 100644 --- a/doc/htmldoc/tutorials/pynest_tutorial/part_3_connecting_networks_with_synapses.rst +++ b/doc/htmldoc/tutorials/pynest_tutorial/part_3_connecting_networks_with_synapses.rst @@ -119,7 +119,7 @@ parameters associated with the distribution can be set (for example nest.Connect(epop1, neuron, "all_to_all", syn_dict) Available distributions and associated parameters are described in -:ref:`Connection Management `, the most common +:ref:`parametrization `, the most common ones are: +-------------------+------------------------+ diff --git a/doc/htmldoc/whats_new/v3.7/index.rst b/doc/htmldoc/whats_new/v3.7/index.rst index eeae9ad1df..66bf1be5ea 100644 --- a/doc/htmldoc/whats_new/v3.7/index.rst +++ b/doc/htmldoc/whats_new/v3.7/index.rst @@ -43,7 +43,8 @@ See connectivity documentation: Connectivity concepts --------------------- -The documentation on :ref:`connection_management` now serves as a living reference for the connection rules defined in the article "Connectivity concepts in neuronal network modeling" [1]_. + +The documentation on :ref:`connectivity_concept` now serves as a living reference for the connection rules defined in the article "Connectivity concepts in neuronal network modeling" [1]_. References ---------- diff --git a/models/multimeter.h b/models/multimeter.h index b22190c5a2..87ae64cd1f 100644 --- a/models/multimeter.h +++ b/models/multimeter.h @@ -96,8 +96,8 @@ it should record from by using the standard ``Connect`` routine. nest.Connect(mm, neurons) To learn more about possible connection patterns and additional -options when using ``Connect``, see the guide on :ref:`connection -management `. +options when using ``Connect``, see the guide on :ref:`connectivity +concept `. The above call to ``Connect`` would fail if the neurons would not support the sampling of the values ``V_m`` and ``g_ex``. It would also From 30fbfe681d9f477a27e2991aabc6506e158c58a7 Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Fri, 8 Dec 2023 09:49:23 +0100 Subject: [PATCH 25/37] fix links --- doc/htmldoc/installation/cmake_options.rst | 2 +- pynest/nest/lib/hl_api_connections.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/htmldoc/installation/cmake_options.rst b/doc/htmldoc/installation/cmake_options.rst index ad8090062c..3ca0dd2785 100644 --- a/doc/htmldoc/installation/cmake_options.rst +++ b/doc/htmldoc/installation/cmake_options.rst @@ -262,7 +262,7 @@ follwing switch for the invocation of ``cmake``. It expects either -Dwith-libneurosim=[OFF|ON|] For details on how to use the Connection Generator Interface, see the -:ref:`guide on connection generation `. +:ref:`guide on connection generation `. .. _compile_with_python: diff --git a/pynest/nest/lib/hl_api_connections.py b/pynest/nest/lib/hl_api_connections.py index 7a2fc8b47d..acf4e86c29 100644 --- a/pynest/nest/lib/hl_api_connections.py +++ b/pynest/nest/lib/hl_api_connections.py @@ -203,7 +203,7 @@ def Connect(pre, post, conn_spec=None, syn_spec=None, return_synapsecollection=F See Also --------- - :ref:`connection_management` + :ref:`connectivity_concept` """ use_connect_arrays, pre, post = _process_input_nodes(pre, post, conn_spec) @@ -367,7 +367,7 @@ def TripartiteConnect(pre, post, third, conn_spec, syn_specs=None): See Also --------- - :ref:`connection_management` + :ref:`connectivity_concept` """ # Confirm that we got node collections From 57d454393218225237d9feffbda0b2310e118643 Mon Sep 17 00:00:00 2001 From: Johanna Senk Date: Thu, 4 Apr 2024 15:06:48 +0200 Subject: [PATCH 26/37] Rename file --- .../{connectivity_concept.rst => connectivity_concepts.rst} | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) rename doc/htmldoc/synapses/{connectivity_concept.rst => connectivity_concepts.rst} (98%) diff --git a/doc/htmldoc/synapses/connectivity_concept.rst b/doc/htmldoc/synapses/connectivity_concepts.rst similarity index 98% rename from doc/htmldoc/synapses/connectivity_concept.rst rename to doc/htmldoc/synapses/connectivity_concepts.rst index fbbe939ecf..64be175349 100644 --- a/doc/htmldoc/synapses/connectivity_concept.rst +++ b/doc/htmldoc/synapses/connectivity_concepts.rst @@ -110,7 +110,7 @@ The mathematical details are extracted from the study on connectivity concepts [ | Let :math:`\mathcal{S}=\{s_1,\ldots, s_{N_s}\}` be the ordered set of sources of cardinality :math:`N_s` and :math:`\mathcal{T}=\{t_1,\ldots, t_{N_t}\}` the set of targets of cardinality :math:`N_t`. Then the set of all possible directed edges between members of :math:`\mathcal{S}` and :math:`\mathcal{T}` is given by the Cartesian product :math:`\mathcal{E}_{ST}=\mathcal{S \times T}` of cardinality :math:`N_s\cdot N_t`. | - | If the source and target populations are identical (:math:`\mathcal{S= T}`) a source can be its own target. We call such a self-connection an :ref:`autapse `. If autapses are not allowed, the target set for any node :math:`i \in \mathcal{S}` is :math:`\mathcal{T=S} \setminus i`, with cardinality :math:`N_t=N_s-1`. If there is more than one edge between a source and target (or from a node to itself), we call this a :ref:`multapse `. + | If the source and target populations are identical (:math:`\mathcal{S=T}`) a source can be its own target. We call such a self-connection an :ref:`autapse `. If autapses are not allowed, the target set for any node :math:`i \in \mathcal{S}` is :math:`\mathcal{T=S} \setminus i`, with cardinality :math:`N_t=N_s-1`. If there is more than one edge between a source and target (or from a node to itself), we call this a :ref:`multapse `. | | The `degree distribution` :math:`P(k)` is the distribution across nodes of the number of edges per node. In a directed network, the distribution of the number of edges going out of (into) a node is called the `out-degree (in-degree)` distribution. The distributions given below describe the effect of applying a connection rule once to a given :math:`\mathcal{S}-\mathcal{T}` pair. @@ -238,7 +238,7 @@ Note that multapses cannot be produced with this rule because each possible edge .. code-block:: python n, m, p = 5, 5, 0.5 - S= nest.Create('iaf_psc_alpha', n) + S = nest.Create('iaf_psc_alpha', n) T = nest.Create('iaf_psc_alpha', m) conn_spec = {'rule': 'pairwise_bernoulli', 'p': p} nest.Connect(S, T, conn_spec) From dfc46f7069573bc236cd89094e91be25946279d8 Mon Sep 17 00:00:00 2001 From: Johanna Senk Date: Thu, 4 Apr 2024 15:17:21 +0200 Subject: [PATCH 27/37] Minor fix --- doc/htmldoc/get-started_index.rst | 1 - doc/htmldoc/static/css/custom.css | 2 +- 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/doc/htmldoc/get-started_index.rst b/doc/htmldoc/get-started_index.rst index ccdea7d3c1..2bf2698ac4 100644 --- a/doc/htmldoc/get-started_index.rst +++ b/doc/htmldoc/get-started_index.rst @@ -61,7 +61,6 @@ Next steps: Create your own network model |random| Connect neurons, synapses, and devices ^^^ - * :ref:`connectivity_concepts`: A guide to define network connectivity in NEST * :ref:`spatial_networks`: A deep dive into building 2D and 3D networks * :ref:`connection_generator`: Using an external library for generating connections diff --git a/doc/htmldoc/static/css/custom.css b/doc/htmldoc/static/css/custom.css index c3f2a35398..8937a43743 100644 --- a/doc/htmldoc/static/css/custom.css +++ b/doc/htmldoc/static/css/custom.css @@ -738,7 +738,7 @@ header{ header{ background-color: var(--nest-orange); padding:0 0; - height: 210px; + height: 180px; } #banner{ padding-top: 50px; From 9c46bf4ad849f7314981cd0b8b53c362cf7631a0 Mon Sep 17 00:00:00 2001 From: Johanna Senk Date: Thu, 4 Apr 2024 16:04:49 +0200 Subject: [PATCH 28/37] Accept suggestion by heplesser Co-authored-by: Hans Ekkehard Plesser --- doc/htmldoc/nest_behavior/running_simulations.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/htmldoc/nest_behavior/running_simulations.rst b/doc/htmldoc/nest_behavior/running_simulations.rst index 1f4c7dca0b..8d4ec785bd 100644 --- a/doc/htmldoc/nest_behavior/running_simulations.rst +++ b/doc/htmldoc/nest_behavior/running_simulations.rst @@ -129,7 +129,7 @@ NEST also has some models that determine the precise time of the threshold crossing during the interval. Please see the documentation on :ref:`precise spike time neurons ` for details about neuron update in continuous time and the -:ref:`documentation on the connectivty concept ` +:ref:`documentation on the connectivty concepts ` for how to set the delay when creating synapses. .. _stepped_simulations: From 54d47d0224501f5c66fc33b9cd228dec181b2f30 Mon Sep 17 00:00:00 2001 From: Johanna Senk Date: Thu, 4 Apr 2024 16:05:16 +0200 Subject: [PATCH 29/37] Accept suggestion by heplesser Co-authored-by: Hans Ekkehard Plesser --- doc/htmldoc/synapses/connectivity_concepts.rst | 1 - 1 file changed, 1 deletion(-) diff --git a/doc/htmldoc/synapses/connectivity_concepts.rst b/doc/htmldoc/synapses/connectivity_concepts.rst index 64be175349..bd0a03bf54 100644 --- a/doc/htmldoc/synapses/connectivity_concepts.rst +++ b/doc/htmldoc/synapses/connectivity_concepts.rst @@ -50,7 +50,6 @@ Connectivity concepts Basic connection rules commonly used in the computational neuroscience community. For more details, go to the section :ref:`conn_rules` or just click on one of the illustrations. - This documentation not only describes how to define network connectivity in NEST, but also provides details on connectivity concepts. The article "Connectivity concepts in neuronal network modeling" [1]_ serves as a permanent reference for a number of connection rules and we suggest to cite it if rules defined there are used. This documentation instead represents a living reference for these rules, and deviations from and extensions to what is described in the article will be highlighted. From 10046272d32ed05eb7bfc704010b937822b7d96d Mon Sep 17 00:00:00 2001 From: Johanna Senk Date: Thu, 4 Apr 2024 16:08:24 +0200 Subject: [PATCH 30/37] Accept suggestion by heplesser Co-authored-by: Hans Ekkehard Plesser --- .../part_1_neurons_and_simple_neural_networks.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/htmldoc/tutorials/pynest_tutorial/part_1_neurons_and_simple_neural_networks.rst b/doc/htmldoc/tutorials/pynest_tutorial/part_1_neurons_and_simple_neural_networks.rst index 380845275d..a14057979f 100644 --- a/doc/htmldoc/tutorials/pynest_tutorial/part_1_neurons_and_simple_neural_networks.rst +++ b/doc/htmldoc/tutorials/pynest_tutorial/part_1_neurons_and_simple_neural_networks.rst @@ -510,7 +510,7 @@ Connections This is an abbreviated version of the documentation for the :py:func:`.Connect` function, please see NEST's online help for the full version and -:ref:`Connectivity concept ` for an introduction +:ref:`Connectivity concepts ` for an introduction and examples. - ``Connect(pre, post, conn_spec=None, syn_spec=None, return_synapsecollection=False)`` From c2b3bd84a0db103ea07770c74883e2e2ef005971 Mon Sep 17 00:00:00 2001 From: Johanna Senk Date: Thu, 4 Apr 2024 16:08:43 +0200 Subject: [PATCH 31/37] Accept suggestion by heplesser Co-authored-by: Hans Ekkehard Plesser --- doc/htmldoc/synapses/connection_generator.rst | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/doc/htmldoc/synapses/connection_generator.rst b/doc/htmldoc/synapses/connection_generator.rst index 77e3e9eda3..29c4fe6824 100644 --- a/doc/htmldoc/synapses/connection_generator.rst +++ b/doc/htmldoc/synapses/connection_generator.rst @@ -30,11 +30,12 @@ generator interface and randomly connects 10% of the neurons from .. code-block:: python + import csa + A = nest.Create('iaf_psc_alpha', 100) B = nest.Create('iaf_psc_alpha', 100) # Create the Connection Generator object - import csa cg = csa.cset(csa.random(0.1), 10000.0, 1.0) # Map weight and delay indices to values from cg From 2475cd15661aa049c2ce18e26498aad5aacb2ca6 Mon Sep 17 00:00:00 2001 From: Johanna Senk Date: Thu, 4 Apr 2024 16:08:59 +0200 Subject: [PATCH 32/37] Update models/multimeter.h Co-authored-by: Hans Ekkehard Plesser --- models/multimeter.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/models/multimeter.h b/models/multimeter.h index 87ae64cd1f..027387f093 100644 --- a/models/multimeter.h +++ b/models/multimeter.h @@ -97,7 +97,7 @@ it should record from by using the standard ``Connect`` routine. To learn more about possible connection patterns and additional options when using ``Connect``, see the guide on :ref:`connectivity -concept `. +concepts `. The above call to ``Connect`` would fail if the neurons would not support the sampling of the values ``V_m`` and ``g_ex``. It would also From 0b0821de5db905b2df513a95d26f93d19e84c18c Mon Sep 17 00:00:00 2001 From: Johanna Senk Date: Thu, 4 Apr 2024 16:01:09 +0200 Subject: [PATCH 33/37] Uncomment sentence about graphical notation in NEST Desktop --- doc/htmldoc/synapses/connectivity_concepts.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/htmldoc/synapses/connectivity_concepts.rst b/doc/htmldoc/synapses/connectivity_concepts.rst index bd0a03bf54..4831216a57 100644 --- a/doc/htmldoc/synapses/connectivity_concepts.rst +++ b/doc/htmldoc/synapses/connectivity_concepts.rst @@ -54,7 +54,7 @@ This documentation not only describes how to define network connectivity in NEST The article "Connectivity concepts in neuronal network modeling" [1]_ serves as a permanent reference for a number of connection rules and we suggest to cite it if rules defined there are used. This documentation instead represents a living reference for these rules, and deviations from and extensions to what is described in the article will be highlighted. -.. The same article also introduces a graphical notation for neuronal network diagrams which will be curated in the documentation of :doc:`NEST Desktop `. +The same article also introduces a graphical notation for neuronal network diagrams which will be curated in the documentation of :doc:`NEST Desktop `. :ref:`spatial_networks` are described on a separate page. From e770ac6db5e9991dae1cb293a3fe0f6bf0d49a00 Mon Sep 17 00:00:00 2001 From: Johanna Senk Date: Thu, 4 Apr 2024 16:13:05 +0200 Subject: [PATCH 34/37] Present tense --- doc/htmldoc/synapses/connectivity_concepts.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/htmldoc/synapses/connectivity_concepts.rst b/doc/htmldoc/synapses/connectivity_concepts.rst index 4831216a57..a1f6ed51c3 100644 --- a/doc/htmldoc/synapses/connectivity_concepts.rst +++ b/doc/htmldoc/synapses/connectivity_concepts.rst @@ -54,7 +54,7 @@ This documentation not only describes how to define network connectivity in NEST The article "Connectivity concepts in neuronal network modeling" [1]_ serves as a permanent reference for a number of connection rules and we suggest to cite it if rules defined there are used. This documentation instead represents a living reference for these rules, and deviations from and extensions to what is described in the article will be highlighted. -The same article also introduces a graphical notation for neuronal network diagrams which will be curated in the documentation of :doc:`NEST Desktop `. +The same article also introduces a graphical notation for neuronal network diagrams which is curated in the documentation of :doc:`NEST Desktop `. :ref:`spatial_networks` are described on a separate page. From f54d67222336182821f4e635d5fa017892623aad Mon Sep 17 00:00:00 2001 From: Johanna Senk Date: Thu, 4 Apr 2024 16:21:27 +0200 Subject: [PATCH 35/37] Lower case for connection generator interface --- doc/htmldoc/synapses/connection_generator.rst | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/doc/htmldoc/synapses/connection_generator.rst b/doc/htmldoc/synapses/connection_generator.rst index 29c4fe6824..dcacacf607 100644 --- a/doc/htmldoc/synapses/connection_generator.rst +++ b/doc/htmldoc/synapses/connection_generator.rst @@ -9,22 +9,21 @@ Connection generator interface :ref:`support for libneurosim `. To allow the generation of connectivity by means of an external -library, NEST supports the Connection Generator Interface [2]_. For +library, NEST supports the connection generator interface [2]_. For more details on this interface, see the git repository of `libneurosim `_. In contrast to the other rules for creating connections, this rule relies on a Connection Generator object to describe the connectivity -pattern in a library-specific way. The Connection Generator is handed +pattern in a library-specific way. The connection generator is handed to :py:func:`.Connect` under the key ``cg`` of the connection specification -dictionary and evaluated internally. If the Connection Generator +dictionary and evaluated internally. If the connection generator provides values for connection weights and delays, their respective indices can be specified under the key ``params_map``. Alternatively, all synapse parameters can be specified using the synapse specification argument to ``Connect()``. -The following listing shows an example for using CSA (`Connection Set Algebra `_ [1]_) in NEST via the Connection -generator interface and randomly connects 10% of the neurons from +The following listing shows an example for using CSA (`Connection Set Algebra `_ [1]_) in NEST via the connection generator interface and randomly connects 10% of the neurons from ``A`` to the neurons in ``B``, each connection having a weight of 10000.0 pA and a delay of 1.0 ms: From 431afb484cc7dbf52bcb1814a816474a84d274bd Mon Sep 17 00:00:00 2001 From: Jessica Mitchell Date: Thu, 4 Apr 2024 20:55:38 +0200 Subject: [PATCH 36/37] re-add pairwise poisson --- .../synapses/connectivity_concepts.rst | 35 +++++++++++++++---- doc/htmldoc/whats_new/v3.7/index.rst | 24 ++++++------- 2 files changed, 41 insertions(+), 18 deletions(-) diff --git a/doc/htmldoc/synapses/connectivity_concepts.rst b/doc/htmldoc/synapses/connectivity_concepts.rst index a1f6ed51c3..d8afa2e416 100644 --- a/doc/htmldoc/synapses/connectivity_concepts.rst +++ b/doc/htmldoc/synapses/connectivity_concepts.rst @@ -116,7 +116,7 @@ The mathematical details are extracted from the study on connectivity concepts [ .. _autapse_multapse: Autapses and multapses ----------------------------------------------- +---------------------- .. image:: ../static/img/Autapse_multapse.png :width: 450px @@ -135,7 +135,8 @@ switches were set to ``False`` in each individual call. .. _deterministic_rules: Deterministic connection rules -------------------------------------------------- +------------------------------ + Deterministic connection rules establish precisely defined sets of connections without any variability across network realizations. .. _one_to_one: @@ -218,7 +219,8 @@ Connections between explicit lists of source-target pairs can be realized in NES .. _probabilistic_rules: Probabilistic connection rules ------------------------------------------------- +------------------------------ + Probabilistic connection rules establish edges according to a probabilistic rule. Consequently, the exact connectivity varies with realizations. Still, such connectivity leads to specific expectation values of network characteristics, such as degree distributions or correlation structure. .. _pairwise_bernoulli: @@ -278,12 +280,32 @@ must be ``True``. 'allow_autapses': False, 'make_symmetric': True} nest.Connect(S, T, conn_spec) -.. _fixed_total_number: +.. _pairwise_poisson: + +Pairwise Poisson +~~~~~~~~~~~~~~~~ +For each possible pair of nodes from ``A`` and ``B``, a number of +connections is created following a Poisson distribution with mean +``pairwise_avg_num_conns``. This means that even for a small +average number of connections between single neurons in ``A`` and +``B`` multiple connections are possible. Thus, for this rule +``allow_multapses`` cannot be ``False``. +The ``pairwise_avg_num_conns`` can be greater than one. +.. code-block:: python + + n, m, p_avg_num_conns = 10, 12, 0.2 + A = nest.Create('iaf_psc_alpha', n) + B = nest.Create('iaf_psc_alpha', m) + conn_spec_dict = {'rule': 'pairwise_poisson', + 'pairwise_avg_num_conns': p_avg_num_conns} + nest.Connect(A, B, conn_spec_dict) + +.. _fixed_total_number: Random, fixed total number -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~ .. image:: ../static/img/Fixed_total_number.png :width: 200px @@ -441,7 +463,7 @@ As multapses are per default allowed and possible with this rule, you can disall .. _fixed_outdegree: Random, fixed out-degree -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~ .. image:: ../static/img/Fixed_outdegree.png :width: 200px @@ -578,6 +600,7 @@ connected to one node in ``T``. References ---------- + .. [1] Senk J, Kriener B, Djurfeldt M, Voges N, Jiang HJ, et al. (2022) Connectivity concepts in neuronal network modeling. PLOS Computational Biology 18(9): e1010086. https://doi.org/10.1371/journal.pcbi.1010086 .. [2] Djurfeldt M. The Connection-set Algebra—A Novel Formalism for the Representation of Connectivity Structure in Neuronal Network Models. Neuroinformatics. 2012; 10: 287–304. https://doi.org/10.1007/s12021-012-9146-1 .. [3] Albert R, Barabási AL. Statistical mechanics of complex networks. Rev Mod Phys. 2002; 74: 47–97. https://doi.org/10.1103/RevModPhys.74.47 diff --git a/doc/htmldoc/whats_new/v3.7/index.rst b/doc/htmldoc/whats_new/v3.7/index.rst index 49fa8c69fe..c260b7b1fc 100644 --- a/doc/htmldoc/whats_new/v3.7/index.rst +++ b/doc/htmldoc/whats_new/v3.7/index.rst @@ -54,6 +54,17 @@ See connectivity documentation: * :ref:`tripartite_connectivity` +New connection rule: ``pairwise_poisson`` +------------------------------------------ + +The number of synapses between pre- and post-synaptic neurons is drawn from a Poisson distribution. +The ``pairwise_poisson`` method is adapted from the ``pairwise bernouilli`` method. + + +See more information: + +* :ref:`pairwise_poisson` + Ignore-and-fire neuron model ---------------------------- @@ -78,17 +89,6 @@ into the neuron model. In particular, It is based on the ``iaf_psc_exp`` model. -New connection rule: ``pairwise_poisson`` ------------------------------------------- - -The number of synapses between pre- and post-synaptic neurons is drawn from a Poisson distribution. -The ``pairwise_poisson`` method is adapted from the ``pairwise bernouilli`` method. - - -See more information: - -* :ref:`connection_management` - New parameter for compartmental model ------------------------------------- @@ -125,6 +125,7 @@ recent C++ compilers should do so. References ---------- + .. [1] Bellec G, Scherr F, Subramoney F, Hajek E, Salaj D, Legenstein R, Maass W (2020). A solution to the learning dilemma for recurrent networks of spiking neurons. Nature Communications, 11:3625. @@ -135,4 +136,3 @@ References .. [3] Tsodyks M, Uziel A, Markram H (2000). Synchrony generation in recurrent networks with frequency-dependent synapses. Journal of Neuroscience, 20 RC50. URL: http://infoscience.epfl.ch/record/183402 - From d01691c3a6b729201799512e85910305a4c8f972 Mon Sep 17 00:00:00 2001 From: Johanna Senk Date: Fri, 5 Apr 2024 09:21:17 +0200 Subject: [PATCH 37/37] Align variable names of Pairwise Poisson with other rules --- doc/htmldoc/synapses/connectivity_concepts.rst | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/doc/htmldoc/synapses/connectivity_concepts.rst b/doc/htmldoc/synapses/connectivity_concepts.rst index d8afa2e416..b50e086cf3 100644 --- a/doc/htmldoc/synapses/connectivity_concepts.rst +++ b/doc/htmldoc/synapses/connectivity_concepts.rst @@ -285,22 +285,22 @@ must be ``True``. Pairwise Poisson ~~~~~~~~~~~~~~~~ -For each possible pair of nodes from ``A`` and ``B``, a number of +For each possible pair of nodes from ``S`` and ``T``, a number of connections is created following a Poisson distribution with mean ``pairwise_avg_num_conns``. This means that even for a small -average number of connections between single neurons in ``A`` and -``B`` multiple connections are possible. Thus, for this rule +average number of connections between single neurons in ``S`` and +``T`` multiple connections are possible. Thus, for this rule ``allow_multapses`` cannot be ``False``. The ``pairwise_avg_num_conns`` can be greater than one. .. code-block:: python n, m, p_avg_num_conns = 10, 12, 0.2 - A = nest.Create('iaf_psc_alpha', n) - B = nest.Create('iaf_psc_alpha', m) - conn_spec_dict = {'rule': 'pairwise_poisson', - 'pairwise_avg_num_conns': p_avg_num_conns} - nest.Connect(A, B, conn_spec_dict) + S = nest.Create('iaf_psc_alpha', n) + T = nest.Create('iaf_psc_alpha', m) + conn_spec = {'rule': 'pairwise_poisson', + 'pairwise_avg_num_conns': p_avg_num_conns} + nest.Connect(S, T, conn_spec) .. _fixed_total_number: