currents = range(Imin, Imax, dI)
frequency = np.zeros(len(currents))
print "Running Simulations"
results = []
for c in range(nTests):
selection = rng.randint(0, npoints, len(originalConstants))
constants = np.zeros(len(originalConstants))
for s in range(len(originalConstants)):
constants[s] = originalConstants[s, selection[s]]
network.set_constants(constants)
try:
for i in range(len(currents)):
spikes, V, W = network.run(duration, currents[i])
spikes = spikes[spikes > 0]
try:
period = (spikes[-2] - spikes[-3] + 0.0) * del_t
frequency[i] = 1000.0 / period
except:
print "frequency to low to measure"
if sum(frequency > 0) > 10:
results.append([constants, frequency, currents])
except:
print "network failed to run"
print "Search Complete"
print len(results), "variations found"
Script to test the neuronal network created by nn.py
"""
import numpy as np
from bnn import BNN
import matplotlib.pyplot as plt
#Set random seed for reproducability
rng = np.random.RandomState(42)
#Parameters for network
num_neurons = 1
duration = 1000
del_t = 0.01
print "Creating Network"
network = BNN(num_neurons=num_neurons,
connectome=np.zeros((num_neurons, num_neurons)),
del_t=del_t)
print "Running Network"
spikes, V, W = network.run(duration, 40)
print "Displaying Results"
plt.figure(1)
plt.scatter(spikes[:,1], spikes[:,0])
plt.figure(2)
plt.plot(V[:,5])
