import numpy.random as rand
import matplotlib.pyplot as plt
p_range = [0, 0.1, 0.2, 0.3, 0.4, 0.5] # rewiring probabilities
n_ex_layer=8
N_ex=100
N_in=200
T = 1000 # Simulation time
Ib = 15.0 # extra current to be injected
window = 50 # mean firing rate averaging window
shift = 20 # mean firing rate shift
step = 10 # bin size in histogram
start_time = 0 # starting time for counting mean firing rate
for p in p_range:
net = create_izModularNetwork(p)
############# Q1(a) #############
vertical = {}
for i in xrange(len(net.layer)):
vertical[i] = np.concatenate(net.layer[i].S.values(), axis=1)
network_matrix = np.concatenate(vertical.values(), axis=0)
plt.matshow(network_matrix, fignum=100, cmap=plt.cm.gray)
plt.title('Connection matrix (p=%.1f)' %p)
# plt.show()
plt.savefig('q1a_%.1f.svg' % p)
plt.clf()
############# Q1(b) #############
from IzModularNetwork import create_izModularNetwork
import numpy as np
import numpy.random as rand
import matplotlib.pyplot as plt
############# Q2 #############
T = 1000 * 5 # Simulation time
Ib = 15.0 # extra current to be injected
p = rand.random()
net = create_izModularNetwork(p)
## Initialise layers
for lr in xrange(len(net.layer)):
net.layer[lr].v = -65 * np.ones(net.layer[lr].N)
net.layer[lr].u = net.layer[lr].b * net.layer[lr].v
net.layer[lr].firings = np.array([])
## SIMULATE
for t in xrange(T):
# Deliver random background current to exhibitory layers
for i in xrange(len(net.layer)-1):
net.layer[i].I = Ib * rand.poisson(0.01, net.layer[i].N)
net.layer[8].I = np.zeros(200)
