python-snn/spike_train at master · 2sukhyunlee/python-snn · GitHub

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######################################################## README #############################################################

# This file generates rate based spike train from the potential map.

############################################################################################################################
\
import numpy as np
from numpy import interp
from matplotlib import pyplot as plt
import imageio
import math
from snn.parameters import param as par
from snn.recep_field import rf

def encode2(pixels):

    #initializing spike train
    train = []

    for l in range(pixels.shape[0]):
        for m in range(pixels.shape[1]):

            temp = np.zeros([(par.T+1),])

            #calculating firing rate proportional to the membrane potential
            freq = interp(pixels[l][m], [0, 255], [1,20])
            #print(pot[l][m], freq)
            # print freq

            assert freq > 0

            freq1 = math.ceil(600/freq)

            #generating spikes according to the firing rate
            k = freq1
            if(pixels[l][m]>0):
                while k<(par.T+1):
                    temp[k] = 1
                    k = k + freq1
            train.append(temp)
            # print sum(temp)
    return train

def encode(pot):

    #initializing spike train
    train = []

    for l in range(pot.shape[0]):
        for m in range(pot.shape[1]):

            temp = np.zeros([(par.T+1),])

            #calculating firing rate proportional to the membrane potential
            freq = interp(pot[l][m], [-1.069,2.781], [1,20])
            #print(pot[l][m], freq)
            # print freq

            assert freq > 0

            freq1 = math.ceil(600/freq)

            #generating spikes according to the firing rate
            k = freq1
            if(pot[l][m]>0):
                while k<(par.T+1):
                    temp[int(k)] = 1
                    k = k + freq1
            train.append(temp)
            # print sum(temp)
    return train

if __name__  == '__main__':
    # m = []
    # n = []
    img = imageio.imread("/Users/johnsoni/Downloads/mnist_png/training/5/0.png")
    #img = imageio.imread("data/training/0.png")

    pot = rf(img)

    # for i in pot:
    #     m.append(max(i))
    #     n.append(min(i))

    # print max(m), min(n)
    #train = encode2(img)
    train = encode(pot)
    f = open('train6.txt', 'w')
    print(np.shape(train))

    for j in range(len(train)):
        for i in range(len(train[j])):
            f.write(str(int(train[j][i])))
        f.write('\n')

    f.close()