1# -*- coding: utf-8 -*- 2# 3# correlospinmatrix_detector_two_neuron.py 4# 5# This file is part of NEST. 6# 7# Copyright (C) 2004 The NEST Initiative 8# 9# NEST is free software: you can redistribute it and/or modify 10# it under the terms of the GNU General Public License as published by 11# the Free Software Foundation, either version 2 of the License, or 12# (at your option) any later version. 13# 14# NEST is distributed in the hope that it will be useful, 15# but WITHOUT ANY WARRANTY; without even the implied warranty of 16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 17# GNU General Public License for more details. 18# 19# You should have received a copy of the GNU General Public License 20# along with NEST. If not, see <http://www.gnu.org/licenses/>. 21 22""" 23Correlospinmatrix detector example 24---------------------------------- 25 26This scripts simulates two connected binary neurons, similar 27as in [1]_. It measures and plots the auto- and cross covariance functions 28of the individual neurons and between them, repsectively. 29 30References 31~~~~~~~~~~ 32 33.. [1] Ginzburg and Sompolinsky (1994). Theory of correlations in stochastic neural networks. 50(4) p. 3175. Fig. 1. 34 35""" 36 37import matplotlib.pyplot as plt 38import nest 39import numpy as np 40 41m_x = 0.5 42tau_m = 10. 43h = 0.1 44T = 1000000. 45tau_max = 100. 46 47csd = nest.Create("correlospinmatrix_detector") 48csd.set(N_channels=2, tau_max=tau_max, Tstart=tau_max, delta_tau=h) 49 50n1 = nest.Create("ginzburg_neuron") 51n1.set(theta=0.0, tau_m=tau_m, c_1=0.0, c_2=2. * m_x, c_3=1.0) 52 53n2 = nest.Create("mcculloch_pitts_neuron") 54n2.set(theta=0.5, tau_m=tau_m) 55 56nest.Connect(n1, n2, syn_spec={"weight": 1.0}) 57 58nest.Connect(n1, csd, syn_spec={"receptor_type": 0}) 59nest.Connect(n2, csd, syn_spec={"receptor_type": 1}) 60 61nest.Simulate(T) 62 63count_covariance = csd.count_covariance 64 65mean_activities = np.zeros(2, dtype=float) 66for i in range(2): 67 mean_activities[i] = count_covariance[i][i][int(tau_max / h)] * (h / T) 68 69print('mean activities =', mean_activities) 70 71covariance_matrix = np.zeros((2, 2, int(2 * tau_max / h) + 1), dtype=float) 72for i in range(2): 73 for j in range(2): 74 covariance_matrix[i, j] = count_covariance[i][j] * (h / T) - mean_activities[i] * mean_activities[j] 75 76ts = np.arange(-tau_max, tau_max + h, h) 77 78plt.title("auto- and cross covariance functions") 79 80plt.plot(ts, covariance_matrix[0, 1], 'r', label=r"$c_{12}$") 81plt.plot(ts, covariance_matrix[1, 0], 'b', label=r"$c_{21}$") 82plt.plot(ts, covariance_matrix[0, 0], 'g', label=r"$c_{11}$") 83plt.plot(ts, covariance_matrix[1, 1], 'y', label=r"$c_{22}$") 84plt.xlabel(r"time $t \; \mathrm{ms}$") 85plt.ylabel(r"$c$") 86plt.legend() 87 88plt.show() 89