# -*- coding: utf-8 -*-
#
# network_params.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# NEST is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with NEST. If not, see .
"""PyNEST Microcircuit: Network Parameters
---------------------------------------------
A dictionary with base network and neuron parameters is enhanced with derived
parameters.
"""
import numpy as np
def get_exc_inh_matrix(val_exc, val_inh, num_pops):
""" Creates a matrix for excitatory and inhibitory values.
Parameters
----------
val_exc
Excitatory value.
val_inh
Inhibitory value.
num_pops
Number of populations.
Returns
-------
matrix
A matrix of of size (num_pops x num_pops).
"""
matrix = np.zeros((num_pops, num_pops))
matrix[:, 0:num_pops:2] = val_exc
matrix[:, 1:num_pops:2] = val_inh
return matrix
net_dict = {
# factor to scale the number of neurons
'N_scaling': 0.1,
# factor to scale the indegrees
'K_scaling': 0.1,
# neuron model
'neuron_model': 'iaf_psc_exp',
# names of the simulated neuronal populations
'populations': ['L23E', 'L23I', 'L4E', 'L4I', 'L5E', 'L5I', 'L6E', 'L6I'],
# number of neurons in the different populations (same order as
# 'populations')
'full_num_neurons':
np.array([20683, 5834, 21915, 5479, 4850, 1065, 14395, 2948]),
# mean rates of the different populations in the non-scaled version of the
# microcircuit (in spikes/s; same order as in 'populations');
# necessary for the scaling of the network.
# The values were obtained by running this PyNEST microcircuit without MPI,
# 'local_num_threads' 4 and both 'N_scaling' and 'K_scaling' set to 1.
'full_mean_rates':
np.array([0.903, 2.965, 4.414, 5.876, 7.569, 8.633, 1.105, 7.829]),
# connection probabilities (the first index corresponds to the targets
# and the second to the sources)
'conn_probs':
np.array(
[[0.1009, 0.1689, 0.0437, 0.0818, 0.0323, 0., 0.0076, 0.],
[0.1346, 0.1371, 0.0316, 0.0515, 0.0755, 0., 0.0042, 0.],
[0.0077, 0.0059, 0.0497, 0.135, 0.0067, 0.0003, 0.0453, 0.],
[0.0691, 0.0029, 0.0794, 0.1597, 0.0033, 0., 0.1057, 0.],
[0.1004, 0.0622, 0.0505, 0.0057, 0.0831, 0.3726, 0.0204, 0.],
[0.0548, 0.0269, 0.0257, 0.0022, 0.06, 0.3158, 0.0086, 0.],
[0.0156, 0.0066, 0.0211, 0.0166, 0.0572, 0.0197, 0.0396, 0.2252],
[0.0364, 0.001, 0.0034, 0.0005, 0.0277, 0.008, 0.0658, 0.1443]]),
# mean amplitude of excitatory postsynaptic potential (in mV)
'PSP_exc_mean': 0.15,
# relative standard deviation of the weight
'weight_rel_std': 0.1,
# relative inhibitory weight
'g': -4,
# mean delay of excitatory connections (in ms)
'delay_exc_mean': 1.5,
# mean delay of inhibitory connections (in ms)
'delay_inh_mean': 0.75,
# relative standard deviation of the delay of excitatory and
# inhibitory connections
'delay_rel_std': 0.5,
# turn Poisson input on or off (True or False)
# if False: DC input is applied for compensation
'poisson_input': True,
# indegree of external connections to the different populations (same order
# as in 'populations')
'K_ext': np.array([1600, 1500, 2100, 1900, 2000, 1900, 2900, 2100]),
# rate of the Poisson generator (in spikes/s)
'bg_rate': 8.,
# delay from the Poisson generator to the network (in ms)
'delay_poisson': 1.5,
# initial conditions for the membrane potential, options are:
# 'original': uniform mean and standard deviation for all populations as
# used in earlier implementations of the model
# 'optimized': population-specific mean and standard deviation, allowing a
# reduction of the initial activity burst in the network
# (default)
'V0_type': 'optimized',
# parameters of the neuron model
'neuron_params': {
# membrane potential average for the neurons (in mV)
'V0_mean': {'original': -58.0,
'optimized': [-68.28, -63.16, -63.33, -63.45,
-63.11, -61.66, -66.72, -61.43]},
# standard deviation of the average membrane potential (in mV)
'V0_std': {'original': 10.0,
'optimized': [5.36, 4.57, 4.74, 4.94,
4.94, 4.55, 5.46, 4.48]},
# reset membrane potential of the neurons (in mV)
'E_L': -65.0,
# threshold potential of the neurons (in mV)
'V_th': -50.0,
# membrane potential after a spike (in mV)
'V_reset': -65.0,
# membrane capacitance (in pF)
'C_m': 250.0,
# membrane time constant (in ms)
'tau_m': 10.0,
# time constant of postsynaptic currents (in ms)
'tau_syn': 0.5,
# refractory period of the neurons after a spike (in ms)
't_ref': 2.0}}
# derive matrix of mean PSPs,
# the mean PSP of the connection from L4E to L23E is doubled
PSP_matrix_mean = get_exc_inh_matrix(
net_dict['PSP_exc_mean'],
net_dict['PSP_exc_mean'] * net_dict['g'],
len(net_dict['populations']))
PSP_matrix_mean[0, 2] = 2. * net_dict['PSP_exc_mean']
updated_dict = {
# matrix of mean PSPs
'PSP_matrix_mean': PSP_matrix_mean,
# matrix of mean delays
'delay_matrix_mean': get_exc_inh_matrix(
net_dict['delay_exc_mean'],
net_dict['delay_inh_mean'],
len(net_dict['populations']))}
net_dict.update(updated_dict)