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 __call__(self, seed):
"""
create a connectivity matrix (W) and update
the matrix of synaptic weigths (J) according
to the sequence of patterns (Y)
"""
self.seed = seed # update seed
self.W = topology(self.n, self.c, self.seed)
self.J = clipped_Hebbian(self.Y, self.W) # time consuming step
def create_synaptic_weights(seed):
"""
create a matrix of synaptic weigths according to
the clipped_Hebbian rule describied in Gibson & Robinson 1992
returns the matrix of connectivity, the original pattern and
the matrix of synaptic weights
"""
W = topology(ncells, c, seed)
Z = Pattern(ncells, a)
Y = generate(Z, m)
J = clipped_Hebbian(Y,W)
return(W, Z, Y, J)
def create_synaptic_weights(seed):
"""
simulate 10 progressive recalls of a partial
pattern at different dt according to the STDP
rule found experimentally
Returns:
The connectivity matrix
The original pattern to be recall
The matrix of synaptic weights
"""
W = topology(ncells, c, seed)
Z = Pattern(ncells, a)
Y = generate(Z, m)
J = clipped_Hebbian(Y, W)
return(W, Z, Y, J)
# To execute with seed=100 # ./test_model 100 # ========================================================================= from network import topology from firings import Pattern from firings import generate, get_valid, get_spurious, get_overlap from plasticity import clipped_Hebbian from plots import raster_plot import numpy as np import sys myseed = int(sys.argv[1]) # read the first argument of the command ncells = 3000 W = topology(n=ncells, c=0.5, seed=myseed) Z = Pattern(n=ncells, a=0.1) Y = generate(Z, m=50) J = clipped_Hebbian(Y, W) # 8.93 s # Recall X = Y[0] # The initial state is exactly the same as the initial pattern nrecall = 6 spikes = np.empty((nrecall, ncells)) X = Y[0] # The initial state is exactly the same as the initial pattern for i in range(6): # progressive recall in 6 steps
#========================================================================= from network import topology from firings import Pattern, generate from plasticity import clipped_Hebbian 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
