Update to 1.0.1

.gitignore

@@ -128,5 +128,5 @@ examples/bos2016/figures/eigenvalue_trajectories_Bos2016.png
 examples/bos2016/figures/sensitivity_measure_low_gamma_Bos2016.png
 examples/bos2016/figures/sensitivity_measure_high_gamma_Bos2016.png
 examples/bos2016/power_spectra_of_subcircuits_Bos2016.png
-examples/schuecker2015/transfer_functions_Schuecker2015.png
+examples/schuecker2015/*.eps
 docs/source/auto_examples

docs/source/bib/nnmt.bib

@@ -1,3 +1,25 @@
+@book{Abramowitz74,
+  author =       {Abramowitz, Milton and Stegun, Irene A.},
+  title =        {Handbook of Mathematical Functions: with Formulas, Graphs, and Mathematical Tables},
+  publisher =    {Dover Publications},
+  year =         {1974},
+  address =      {New York},
+  lastname =     {Abramowitz},
+  doi = {10.1119/1.15378},
+  url = {https://doi.org/10.1119/1.15378},
+}
+
+@article{amit1991,
+  title={Quantitative study of attractor neural network retrieving at low spike rates. I. Substrate-spikes, rates and neuronal gain},
+  author={Amit, Daniel J and Tsodyks, MV},
+  journal={Network: Computation in neural systems},
+  volume={2},
+  number={3},
+  pages={259},
+  year={1991},
+  publisher={IOP Publishing}
+}
+
 @article{amit1997,
   title={Dynamics of a recurrent network of spiking neurons before and following learning},
   author={Amit, Daniel J and Brunel, Nicolas},
@@ -112,3 +134,10 @@ @article{siegert1951
   publisher={APS}
 }
 
+@book{tuckwell1988,
+  title={Introduction to theoretical neurobiology: linear cable theory and dendritic structure},
+  author={Tuckwell, Henry Clavering},
+  volume={1},
+  year={1988},
+  publisher={Cambridge University Press}
+}

docs/source/bos_2016_index.rst

@@ -23,14 +23,22 @@ These examples reproduce results by :cite:t:`bos2016`.
   :width: 400
   :alt: Eigenvalue Trajectories (Fig. 4 in :cite:t:`bos2016`)
 
-- :doc:`sensitivity_measure.py <auto_examples/bos2016/sensitivity_measure>`:
+- :doc:`sensitivity_measure_high_gamma.py <auto_examples/bos2016/sensitivity_measure_high_gamma>`:
   computes the sensitivity measure for each eigenmode to reveal the anatomical origin
   of peaks in the power spectra (Fig. 6 in :cite:t:`bos2016`)
 
 .. image:: ../../examples/bos2016/figures/sensitivity_measure_high_gamma_Bos2016.png
   :width: 400
   :alt: Sensitivity Measure - High-$\gamma$ frequencies (Fig. 6 in :cite:t:`bos2016`)
 
+- :doc:`sensitivity_measure_low_gamma.py <auto_examples/bos2016/sensitivity_measure_low_gamma>`:
+  computes the sensitivity measure and changes connectivity to reduce peak 
+  in power spectra accordingly (parts of Fig. 5 and Fig .8 in :cite:t:`bos2016`)
+
+.. image:: ../../examples/bos2016/figures/sensitivity_measure_low_gamma_Bos2016.png
+  :width: 400
+  :alt: Sensitivity Measure - Low-$\gamma$ frequencies (parts of Fig. 5 and Fig.8 in :cite:t:`bos2016`)
+
 - :doc:`power_spectra_of_subcircuits.py <auto_examples/bos2016/power_spectra_of_subcircuits>`:
   confirms the results of the sensitivity measure for the low-$\gamma$ oscillations by
   plotting the power spectra of the relevant subcircuits

docs/source/conf.py

@@ -22,11 +22,11 @@
 # -- Project information -----------------------------------------------------
 
 project = 'NNMT'
-copyright = '2021, Moritz Layer, Johanna Senk, Simon Essink, Alexander van Meegen, Hannah Bos, Moritz Helias'
+copyright = '2022, Moritz Layer, Johanna Senk, Simon Essink, Alexander van Meegen, Hannah Bos, Moritz Helias'
 author = 'Moritz Layer, Johanna Senk, Simon Essink, Alexander van Meegen, Hannah Bos, Moritz Helias'
 
 # The full version, including alpha/beta/rc tags
-release = '1.0.0'
+release = '1.0.1'
 
 
 # -- General configuration ---------------------------------------------------

docs/source/lif.rst

@@ -64,3 +64,64 @@ given by
     = -I_\mathrm{syn}(t)
     + \sum_{i=1}^{N_\mathrm{s}} J_i \sum_k \delta(t-t_i^k) \tau_\mathrm{m} \quad .
 
+*********
+Variables
+*********
+
+Here you find how variables of LIF neurons are named in NNMT:
+
+.. list-table::
+   :widths: 50 25 25
+   :header-rows: 1
+
+   * - Quantity
+     - Symbol
+     - Variable name
+   * - Synaptic delay matrix
+     - :math:`\boldsymbol{D}`
+     - ``D``
+   * - Synaptic weight matrix
+     - :math:`\boldsymbol{J}`
+     - ``J``
+   * - External synaptic weight matrix
+     - :math:`\boldsymbol{J}_\mathrm{ext}`
+     - ``J_ext``
+   * - Indegree matrix
+     - :math:`\boldsymbol{K}`
+     - ``K``
+   * - External indegree matrix
+     - :math:`\boldsymbol{K}_\mathrm{ext}`
+     - ``K_ext``
+   * - Number of neurons
+     - :math:`\boldsymbol{N}`
+     - ``N``
+   * - Reset voltage
+     - :math:`V_0`
+     - ``V_0``
+   * - Threshold voltage
+     - :math:`V_\Theta`
+     - ``V_th``
+   * - Mean of synaptic input
+     - :math:`\boldsymbol{\mu}`
+     - ``mu``
+   * - Population rates
+     - :math:`\boldsymbol{\nu}`
+     - ``nu``
+   * - External population rates
+     - :math:`\boldsymbol{\nu}_\mathrm{ext}`
+     - ``nu_ext``
+   * - Angular frequencies
+     - :math:`\omega`
+     - ``omegas``
+   * - Standard deviation of synaptic input
+     - :math:`\boldsymbol{\sigma}`
+     - ``sigma``
+   * - Membrane time constant
+     - :math:`\tau_\mathrm{m}`
+     - ``tau_m``
+   * - Refractory time
+     - :math:`\tau_\mathrm{r}`
+     - ``tau_r``
+   * - Pre-synaptic time constant
+     - :math:`\tau_\mathrm{s}`
+     - ``tau_s``

docs/source/release_notes.rst

@@ -4,6 +4,21 @@
 Release notes
 =============
 
+**********
+NNMT 1.0.1
+**********
+
+- Deepcopy parameter dictionaries on instantiation of network model. Otherwise
+  dictionary items can change unwantedly if netork parameters are changed.
+- Add approximations and assumptions to docstrings.
+- Add explanation of approximations to docs.
+- Add table of LIF parameters and NNMT variables to docs.
+- Fix description in docstrings for ``tau_s``.
+- Fix typos in docstrings.
+- Add new example of adjusting the low-gamma peak in the microcircuit model.
+- Add pytest and pytest-mock to setup requirements, such that after pip
+  installion the tests can be run.
+
 **********
 NNMT 1.0.0
 **********

docs/source/tools.rst

@@ -13,6 +13,32 @@ parameters direcly as arguments, and respective wrapper tools that expect a
 
 Please read the :ref:`overview <sec_overview>` for more details.
 
+******************************
+Approximations and assumptions
+******************************
+
+Analytical analyses of neuronal netoworks almost always rely on approximations
+and assumptions. If the network analyzed with a mean-field based tool does not
+fulfill the tool's requirements, it cannot provide reliable results. These
+restrictions should be documented in the docstrings of the respective tools.
+Here, we explain a few important terms that appear in that context.
+
+- **Diffusion approximation**: If a neuron receives Poissonian uncorrelated
+  input spike trains and the contribution of a single syanptic connection is
+  small compared to the distance between reset and threshold
+  :math:`w \ll \left(V_\Theta - V_0\right)`, the random input can be
+  approximated by Gaussian white noise with mean :math:`\mu` and noise
+  intensity :math:`\sigma^2` :cite:p:`tuckwell1988,amit1991`. This
+  approximation does not hold if the network features highly correlated
+  activity or receives strong external input common to many neurons.
+- **Linear response theory**: Studies how populations of neurons
+  in the stationary state respond to weak external input, ignoring non-linear
+  interactions. Linear response theory cannot explain higher order effects like
+  the occurence of higher harmonics.
+- **Fast/slow synaptic regime**: Parameter regime in which the synaptic time
+  constant :math:`\tau_\mathrm{s}` is much shorter/longer than the membrane
+  time constant :math:`\tau_\mathrm{m}`.
+
 ******************
 Network properties
 ******************

examples/bos2016/power_spectra.py

@@ -120,7 +120,7 @@
             ax.set_yticks([1e-5,1e-3,1e-1])
 
         if j == 0:
-            ax.set_ylabel(r'power spectrum $|C(\omega)|\quad(1/\mathrm{s}^2)$')
+            ax.set_ylabel(r'power spectrum $P(\omega)\quad(1/\mathrm{s}^2)$')
             ax.legend()
 
 plt.savefig('figures/power_spectra_Bos2016.png')

examples/bos2016/sensitivity_measure.py

@@ -158,7 +158,7 @@ def colorbar(mappable, cax=None):
 
     rounded_frequency = str(int(np.round(frequency,0)))
 
-    plot_title = r'$\mathbf{Z}_{j=%s}^{\mathrm{amp}}(' % ev + \
+    plot_title = r'$\mathbf{Z}_{j=%s}^{\mathrm{freq}}(' % ev + \
         f'{rounded_frequency}' + r'\,\mathrm{Hz})$'
     ax.set_title(plot_title)