import numpy as np recordingTime = np.array([10,20,30,40,50,60,70,80,90,100,110,120,130,140,150,160,170,180,190,200]) tenSpikeTrains = np.array([[0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0],\ [0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,1,0,0,0],\ [1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0],\ [0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,1,0,0],\ [0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0],\ [0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,1,0],\ [0,0,0,0,0,0,0,1,1,1,0,0,1,1,0,0,1,1,0,0],\ [0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0],\ [0,0,0,0,0,0,0,1,1,0,0,1,1,0,0,1,1,0,0,0],\ [0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,1,1,0,0]])

number_of_spikes=[] for j in range (len(tenSpikeTrains[0])): number_of_spikes.append(np.sum(tenSpikeTrains[:,j])) print ("The total number of spikesfor each time bin is",number_of_spikes)

import matplotlib.pyplot as plt plt.bar(recordingTime, number_of_spikes, color="black",width=3) plt.show()

mean_number_of_spikes=[] for j in range (len(tenSpikeTrains[0])): mean_number_of_spikes.append(np.sum(tenSpikeTrains[:,j])) plt.bar(recordingTime,mean_number_of_spikes) plt.show()

print("The average firing rate across trials is",(np.sum(number_of_spikes)*1000)/(recordingTime[-1]*10),'spikes/s')