def simulate(seed, dt):
"""
simulate 10 progressive recall of the original
pattern at different dt according to the STDP
rule found experimentally
"""
ncells = 3000 # number of cells
c = 0.5 # connection probability
a = 0.1 # activity of a pattern
m = 50 # number of patterns to store
g1 = 0.433 # slope of inhibitory component
W = topology(ncells, c, seed)
Z = Pattern(ncells, a)
Y = generate(Z, m)
J = clipped_Hebbian(Y, W)
J = J*delta_t(dt)
overlap = np.empty(10)
X = Y[0] # initial pattern
for i in range(10):
h = np.inner(J.T, X)/float(ncells)
spk_avg = np.mean(X)
X = ( h < g1*spk_avg ).choose(1,0)
overlap[i] = get_overlap(Z, X)
return(overlap)
def calculate_overlap(vector, dt_list):
"""
perform 50 simulations with different seed for the connectivity
matrix (W)
X -- a vector of initial activities
dt -- time between pre and post synaptic timing for plasticity rule
mynetwork = Simulation(3000, 0.5, 0.1, 50, 0.433, 0)
# a vector of 50 simulations
np.savetxt('./data/[dt=%2d].out')
"""
ncells = 3000 # number of cells
c = 0.5 # connection probability
a = 0.1 # activity of a pattern
m = 50 # number of patterns to store
g1 = 0.433 # slope of inhibitory component
nrecall = 10 # progressive recalls
seedlist = np.arange(50, 2500, 50) # 49 seeds
mynetwork = Simulation(ncells, c, a, m, g1, 0 ) # first seed is zero
mynetwork.J = mynetwork.J*delta_t(dt)
# create
for s in seedlist:
mynetwork(seed=s) # create new topology
# compute the firings after 10 recalls
firings = mynetwork.recall(X=vector, nrecall=10)
for t in dt_list:
mynetwor.J = mynetwork
overlaps = [for n in 10 get_overlap(mynetwork.Z, firings[n])]
from plots import raster_plot from STDP import delta_t import numpy as np n = 3000 c = 0.5 a = 0.1 g = 0.433 W = topology(n, c) Z = Pattern(n, a) Y = generate(Z, m = 50) J = clipped_Hebbian(Y, W) # 8.93 s J = J*delta_t(50) # create a random distortion of Z # 10% of the initial ones b1 = 0.1 #bn = 1 X = np.zeros(n, dtype=int) similarity = int(n*a*b1) X[:similarity] = 1 # recovery in 10 steps spikes = np.empty((11, n)) spikes[0] = X for i in range(1, 11):
X = ( h < g1*spk_avg ).choose(1,0)
overlap[i] = get_overlap(Z, X)
return(overlap)
if __name__ == '__main__':
# 50 seeds per simulation
myseed = np.arange(0, 2500, 50)
# 9 timmings
mydt = np.array([-100, -50, -25, -10, 0, 10, 25, 50, 100])
myseed_list = list()
for s in myseed:
W, Z, Y, J = create_synaptic_weights(seed=s)
collecdt = np.empty([len(mydt),11])
for j,t in enumerate(mydt):
Jnew = J*delta_t(t)
collecdt[j] = recall(Z, Jnew)
myseed_list.append(collecdt)
# reorder to save output in files
for t, i in enumerate(mydt):
dt_tmp = [myseed_list[x][t] for x in range(len(myseed_list))]
np.savetxt('./data/overlap[dt=%d].out'%(i), dt_tmp, fmt='%f')
