Merge branch 'fix_tau_m'

docs/source/conf.py

@@ -26,7 +26,7 @@
 author = 'Moritz Layer, Johanna Senk, Simon Essink, Alexander van Meegen, Hannah Bos, Moritz Helias'
 
 # The full version, including alpha/beta/rc tags
-release = '1.0.1'
+release = '1.0.2'
 
 
 # -- General configuration ---------------------------------------------------

docs/source/lif.rst

@@ -116,7 +116,7 @@ Here you find how variables of LIF neurons are named in NNMT:
    * - Standard deviation of synaptic input
      - :math:`\boldsymbol{\sigma}`
      - ``sigma``
-   * - Membrane time constant
+   * - Post-synaptic membrane time constant
      - :math:`\tau_\mathrm{m}`
      - ``tau_m``
    * - Refractory time

docs/source/release_notes.rst

@@ -4,6 +4,20 @@
 Release notes
 =============
 
+**********
+NNMT 1.0.2
+**********
+
+- Fix calculation of mean input and std input for lif.exp. Previously ``tau_m``
+  was multiplied with the firing rates before the dot product with the
+  connectivity. However, as ``tau_m`` is representing the post-synaptic
+  membrane time constant, it should be multiplied after performing the dot
+  product.
+- Fix explanation of ``tau_m`` in docstrings.
+- Add new integration test for lif.exp._firing_rates with vector parameters.
+- Fix docopt usage in fixture creation for unit and integration fixtures.
+
+
 **********
 NNMT 1.0.1
 **********

nnmt/__init__.py

@@ -7,4 +7,4 @@
 from . import models
 from . import lif
 
-__version__ = '1.0.1'
+__version__ = '1.0.2'

nnmt/lif/_general.py

@@ -231,7 +231,7 @@ def _mean_input(nu, J, K, tau_m, J_ext, K_ext, nu_ext):
     K : np.array
         In-degree matrix.
     tau_m : [float | 1d array]
-        Membrane time constant in s.
+        Membrane time constant of post-synatic neuron in s.
     J_ext : np.array
         External weight matrix in V.
     K_ext : np.array
@@ -245,7 +245,7 @@ def _mean_input(nu, J, K, tau_m, J_ext, K_ext, nu_ext):
         Array of mean inputs to each population in V.
     """
     # contribution from within the network
-    m0 = np.dot(K * J, tau_m * nu)
+    m0 = tau_m * np.dot(K * J, nu)
     # contribution from external sources
     m_ext = tau_m * np.dot(K_ext * J_ext, nu_ext)
     # add them up
@@ -268,7 +268,7 @@ def _std_input(nu, J, K, tau_m, J_ext, K_ext, nu_ext):
     K : np.array
         In-degree matrix.
     tau_m : [float | 1d array]
-        Membrane time constant in s.
+        Membrane time constant of post-synatic neuron in s.
     J_ext : np.array
         External weight matrix in V.
     K_ext : np.array
@@ -282,7 +282,7 @@ def _std_input(nu, J, K, tau_m, J_ext, K_ext, nu_ext):
         Array of standard deviation of inputs to each population in V.
     """
     # contribution from within the network to variance
-    var0 = np.dot(K * J**2, tau_m * nu)
+    var0 = tau_m * np.dot(K * J**2, nu)
     # contribution from external sources to variance
     var_ext = tau_m * np.dot(K_ext * J_ext**2, nu_ext)
     # add them up

nnmt/lif/delta.py

@@ -105,7 +105,7 @@ def _firing_rates(J, K, V_0_rel, V_th_rel, tau_m, tau_r, J_ext, K_ext, nu_ext,
     V_th_rel : [float | 1d array]
         Relative threshold potential in V.
     tau_m : [float | 1d array]
-        Membrane time constant in s.
+        Membrane time constant of post-synatic neuron in s.
     tau_r : [float | 1d array]
         Refractory time in s.
     J_ext : np.array
@@ -163,7 +163,7 @@ def _firing_rates_for_given_input(V_0_rel, V_th_rel, mu, sigma, tau_m, tau_r):
     sigma : [float | 1d array]
         Standard deviation of input to population of neurons.
     tau_m : [float | 1d array]
-        Membrane time constant in s.
+        Membrane time constant of post-synatic neuron in s.
     tau_r : [float | 1d array]
         Refractory time in s.
 
@@ -326,7 +326,7 @@ def _mean_input(nu, J, K, tau_m, J_ext, K_ext, nu_ext):
     K : np.array
         In-degree matrix.
     tau_m : [float | 1d array]
-        Membrane time constant in s.
+        Membrane time constant of post-synatic neuron in s.
     J_ext : np.array
         External weight matrix in V.
     K_ext : np.array
@@ -340,7 +340,7 @@ def _mean_input(nu, J, K, tau_m, J_ext, K_ext, nu_ext):
         Array of mean inputs to each population in V.
     """
     return _general._mean_input(nu, J, K, tau_m,
-                               J_ext, K_ext, nu_ext)
+                                J_ext, K_ext, nu_ext)
 
 
 def std_input(network):
@@ -390,7 +390,7 @@ def _std_input(nu, J, K, tau_m, J_ext, K_ext, nu_ext):
     K : np.array
         In-degree matrix.
     tau_m : [float | 1d array]
-        Membrane time constant in s.
+        Membrane time constant of post-synatic neuron in s.
     J_ext : np.array
         External weight matrix in V.
     K_ext : np.array
@@ -404,7 +404,7 @@ def _std_input(nu, J, K, tau_m, J_ext, K_ext, nu_ext):
         Array of mean inputs to each population in V.
     """
     return _general._std_input(nu, J, K, tau_m,
-                              J_ext, K_ext, nu_ext)
+                               J_ext, K_ext, nu_ext)
 
 
 def _derivative_of_firing_rates_wrt_mean_input(V_0_rel, V_th_rel, mu, sigma,
@@ -425,7 +425,7 @@ def _derivative_of_firing_rates_wrt_mean_input(V_0_rel, V_th_rel, mu, sigma,
     sigma : float
         Standard deviation of neuron activity in V.
     tau_m : float
-        Membrane time constant in s.
+        Membrane time constant of post-synatic neuron in s.
     tau_r : float
         Refractory time in s.
 

nnmt/lif/exp.py

@@ -180,7 +180,7 @@ def _firing_rates(J, K, V_0_rel, V_th_rel, tau_m, tau_r, tau_s, J_ext, K_ext,
     V_th_rel : [float | 1d array]
         Relative threshold potential in V.
     tau_m : [float | 1d array]
-        Membrane time constant in s.
+        Membrane time constant of post-synatic neuron in s.
     tau_r : [float | 1d array]
         Refractory time in s.
     tau_s : float
@@ -254,7 +254,7 @@ def _firing_rate_shift(V_0_rel, V_th_rel, mu, sigma, tau_m, tau_r, tau_s):
     sigma : [float | np.array]
         Standard deviation of neuron activity in V.
     tau_m : [float | 1d array]
-        Membrane time constant in s.
+        Membrane time constant of post-synatic neuron in s.
     tau_r : [float | 1d array]
         Refractory time in s.
     tau_s : float
@@ -302,7 +302,7 @@ def _firing_rate_taylor(V_0_rel, V_th_rel, mu, sigma, tau_m, tau_r, tau_s):
     sigma : [float | np.array]
         Standard deviation of neuron activity in V.
     tau_m : [float | 1d array]
-        Membrane time constant in s.
+        Membrane time constant of post-synatic neuron in s.
     tau_r : [float | 1d array]
         Refractory time in s.
     tau_s : float
@@ -436,7 +436,7 @@ def mean_input(network):
     K : np.array
         Indegree matrix.
     tau_m : [float | 1d array]
-        Membrane time constant in s.
+        Membrane time constant of post-synatic neuron in s.
     J_ext : np.array
         External weight matrix in V.
     K_ext : np.array
@@ -479,7 +479,7 @@ def _mean_input(nu, J, K, tau_m, J_ext, K_ext, nu_ext):
     K : np.array
         In-degree matrix.
     tau_m : [float | 1d array]
-        Membrane time constant in s.
+        Membrane time constant of post-synatic neuron in s.
     J_ext : np.array
         External weight matrix in V.
     K_ext : np.array
@@ -543,7 +543,7 @@ def _std_input(nu, J, K, tau_m, J_ext, K_ext, nu_ext):
     K : np.array
         In-degree matrix.
     tau_m : [float | 1d array]
-        Membrane time constant in s.
+        Membrane time constant of post-synatic neuron in s.
     J_ext : np.array
         External weight matrix in V.
     K_ext : np.array
@@ -637,7 +637,7 @@ def _transfer_function(mu, sigma, tau_m, tau_s, tau_r, V_th_rel, V_0_rel,
     sigma : [float | np.array]
         Standard deviation of neuron activity of one population in V.
     tau_m : [float | np.array]
-        Membrane time constant in s.
+        Membrane time constant of post-synatic neuron in s.
     tau_s : float
         Pre-synaptic time constant in s.
     tau_r : [float | np.array]
@@ -703,7 +703,7 @@ def _transfer_function_shift(mu, sigma, tau_m, tau_s, tau_r, V_th_rel,
     sigma : [float | np.array]
         Standard deviation of neuron activity of one population in V.
     tau_m : [float | np.array]
-        Membrane time constant in s.
+        Membrane time constant of post-synatic neuron in s.
     tau_s : float
         Pre-synaptic time constant in s.
     tau_r : [float | np.array]
@@ -795,7 +795,7 @@ def _transfer_function_taylor(mu, sigma, tau_m, tau_s, tau_r, V_th_rel,
     sigma : [float | np.array]
         Standard deviation of neuron activity of one population in V.
     tau_m : [float | np.array]
-        Membrane time constant in s.
+        Membrane time constant of post-synatic neuron in s.
     tau_s : float
         Pre-synaptic time constant in s.
     tau_r : [float | np.array]
@@ -933,7 +933,7 @@ def _fit_transfer_function(transfer_function, omegas, tau_m, J, K):
     omegas : [float | np.ndarray]
         Input frequencies to population in Hz.
     tau_m : float
-        Membrane time constant in s.
+        Membrane time constant of post-synatic neuron in s.
     J : np.array
         Weight matrix in V.
     K : np.array
@@ -1004,7 +1004,7 @@ def _derivative_of_firing_rates_wrt_mean_input(V_0_rel, V_th_rel, mu, sigma,
     Parameters
     ----------
     tau_m : [float | np.ndarray]
-        Membrane time constant in s.
+        Membrane time constant of post-synatic neuron in s.
     tau_s : float
         Pre-synaptic time constant in s.
     tau_r : [float | np.ndarray]
@@ -1053,9 +1053,9 @@ def _Phi(s):
 def _Psi(z, x):
     """
     Calcs Psi(z,x)=exp(x**2/4)*U(z,x), with U(z,x) the parabolic cylinder func.
-    
+
     The mpmath.pcfu() is equivalent to Eq. 19.12.3 in:cite:t:`Abramowitz74`
-    with U(a,-x). The arguments (a, z) of mpmath.pcfu() used in the 
+    with U(a,-x). The arguments (a, z) of mpmath.pcfu() used in the
     documentation https://mpmath.org/doc/current/functions/bessel.html?highlight=pcfu#mpmath.pcfu
     are renamed to (z, x) here.
     """
@@ -1116,7 +1116,7 @@ def _derivative_of_firing_rates_wrt_input_rate(
     sigma :
         Standard deviation of neuron activity in V.
     tau_m : [float | np.ndarray]
-        Membrane time constant in s.
+        Membrane time constant of post-synatic neuron in s.
     tau_s : float
         Pre-synaptic time constant in s.
     tau_r : [float | np.ndarray]
@@ -1232,7 +1232,7 @@ def _effective_connectivity(transfer_function, D, J, K, tau_m):
     K : np.ndarray
         Indegree matrix.
     tau_m : float
-        Membrane time constant in s.
+        Membrane time constant of post-synatic neuron in s.
 
     Returns
     -------
@@ -1790,7 +1790,7 @@ def _power_spectra(nu, effective_connectivity, J, K, N, tau_m):
     N : np.ndarray
         Number of neurons in each population.
     tau_m : [float | np.narray]
-        Membrane time constant in s.
+        Membrane time constant of post-synatic neuron in s.
 
     Returns
     -------
@@ -1883,7 +1883,7 @@ def _external_rates_for_fixed_input(mu_set, sigma_set,
     V_0_rel : [float | np.array]
         Relative reset potential in V.
     tau_m : [float | 1d array]
-        Membrane time constant in s.
+        Membrane time constant of post-synatic neuron in s.
     tau_r : [float | 1d array]
         Refractory time in s.
     tau_s : float

setup.py

@@ -1,7 +1,7 @@
 from setuptools import setup, find_packages
 
 setup(name='nnmt',
-      version='1.0.1',
+      version='1.0.2',
       description='Neuronal Network Meanfield Toolbox',
       long_description=open('README.md').read(),
       long_description_content_type='text/markdown',