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 recall(Z,J):
"""
returns a
"""
# X = INCOMPLETE PATTERN
X = np.zeros(ncells, dtype=int)
# incomplete pattern 10% of valid firings b1=0.1
similarity = int(ncells*a*0.1)
X[:similarity] = 1
# RECALL
nrecall = 10
overlap = np.empty(11, dtype=float)
overlap[0] = get_overlap(Z, X) # first the pattern itself
for i in range(1, nrecall+1):
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)
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
h = np.inner(J.T, X) / float(ncells) # 86.8 ms per loop
print("recall [%d]:" % i),
spk_avg = np.mean(X)
print("threshold = %f" % (0.433 * spk_avg)),
X = (h < 0.433 * spk_avg).choose(1, 0)
spikes[i] = X
print("Firings: valid/spurious %d/%d" % (get_valid(Z, X), get_spurious(Z, X))),
print("Overlap = %f" % get_overlap(Z, X))
raster_plot(spikes)
