def main():
train, spikes = get_spike_train()
# print("Stimulus:", spikes)
ws0 = np.random.normal(loc=w0, scale=sigma0, size=(N))
ws1 = np.random.normal(loc=w1, scale=sigma1, size=(N))
current0 = get_cumulative_current(spikes, ws0)
current1 = get_cumulative_current(spikes, ws1)
model = Model.RS
initU, initV = initalize_U_V(model, M, True)
_, V0, _x0 = get_U_V(initU, initV, model, current0)
_, V1, _x1 = get_U_V(initU, initV, model, current1)
print("Spikes: %d, w_mean: %f, w_std: %f" % (len(_x0), w0, sigma0))
print("Spikes: %d, w_mean: %f, w_std: %f" % (len(_x1), w1, sigma1))
plot_curr_and_resp( current0, V0, "Q2.a.png" )
plot_curr_and_resp( current1, V1, "Q2.b.png" )
def main():
_, spikes1 = get_spike_train()
_, spikes2 = get_spike_train()
# print("S1:", spikes1)
# print("S2:", spikes2)
ws0 = np.random.normal(loc=w0, scale=sigma0, size=(N))
model = Model.RS
initU, initV = initalize_U_V(model, M, True)
print("Q5.a")
currenti1 = get_cumulative_current(spikes1, ws0)
_, V1, aspikes1 = get_U_V(initU, initV, model, currenti1)
currenti2 = get_cumulative_current(spikes2, ws0)
_, V2, aspikes2 = get_U_V(initU, initV, model, currenti2)
plot_curr_and_resp(currenti1, V1, "Q5.a.1.png")
plot_curr_and_resp(currenti2, V2, "Q5.a.2.png")
print("Q5.b")
print("Removing spikes for S2")
wsn = ws0
while len(aspikes2) != 0:
wsn, _, _ = train_d_synapses(wsn, spikes=spikes2, effect=aspikes2)
currenti2 = get_cumulative_current(spikes2, wsn)
_, V2, aspikes2 = get_U_V(initU, initV, model, currenti2)
plot_curr_and_resp(currenti2, V2, "Q5.b.2.png")
print("Q5.c")
print("Causing spikes for S1 and removing spikes for S2")
# main learning loop
wsn = ws0
currenti1 = get_cumulative_current(spikes1, wsn)
_, V1, aspikes1 = get_U_V(initU, initV, model, currenti1)
currenti2 = get_cumulative_current(spikes2, wsn)
_, V2, aspikes2 = get_U_V(initU, initV, model, currenti2)
while len(aspikes1) == 0 or len(aspikes2) != 0:
currenti1 = get_cumulative_current(spikes1, wsn)
_, _, aspikes1 = get_U_V(initU, initV, model, currenti1)
while len(aspikes1) == 0:
wsn, _, _ = train_synapses(wsn, spikes=spikes1, effect=aspikes1)
currenti1 = get_cumulative_current(spikes1, wsn)
_, _, aspikes1 = get_U_V(initU, initV, model, currenti1)
print("Causing spikes for S1")
currenti2 = get_cumulative_current(spikes2, wsn)
_, _, aspikes2 = get_U_V(initU, initV, model, currenti2)
while len(aspikes2) != 0:
wsn, _, _ = train_d_synapses(wsn, spikes=spikes2, effect=aspikes2)
currenti2 = get_cumulative_current(spikes2, wsn)
_, _, aspikes2 = get_U_V(initU, initV, model, currenti2)
print("Removed spikes for S2")
currenti1 = get_cumulative_current(spikes1, wsn)
_, V1, aspikes1 = get_U_V(initU, initV, model, currenti1)
currenti2 = get_cumulative_current(spikes2, wsn)
_, V2, aspikes2 = get_U_V(initU, initV, model, currenti2)
plot_curr_and_resp(currenti1, V1, "Q5.c.1.png")
plot_curr_and_resp(currenti2, V2, "Q5.c.2.png")
print("Q5.d")
print("Removing spikes for S1 and causing spikes for S2")
# main learning loop
wsn = ws0
currenti1 = get_cumulative_current(spikes1, wsn)
_, V1, aspikes1 = get_U_V(initU, initV, model, currenti1)
currenti2 = get_cumulative_current(spikes2, wsn)
_, V2, aspikes2 = get_U_V(initU, initV, model, currenti2)
while len(aspikes1) != 0 or len(aspikes2) == 0:
currenti1 = get_cumulative_current(spikes1, wsn)
_, _, aspikes1 = get_U_V(initU, initV, model, currenti1)
while len(aspikes1) != 0:
wsn, _, _ = train_d_synapses(wsn, spikes=spikes1, effect=aspikes1)
currenti1 = get_cumulative_current(spikes1, wsn)
_, _, aspikes1 = get_U_V(initU, initV, model, currenti1)
print("Removed spikes for S1")
currenti2 = get_cumulative_current(spikes2, wsn)
_, _, aspikes2 = get_U_V(initU, initV, model, currenti2)
while len(aspikes2) == 0:
wsn, _, _ = train_synapses(wsn, spikes=spikes2, effect=aspikes2)
currenti2 = get_cumulative_current(spikes2, wsn)
_, _, aspikes2 = get_U_V(initU, initV, model, currenti2)
print("Caused spikes for S2")
currenti1 = get_cumulative_current(spikes1, wsn)
_, V1, aspikes1 = get_U_V(initU, initV, model, currenti1)
currenti2 = get_cumulative_current(spikes2, wsn)
_, V2, aspikes2 = get_U_V(initU, initV, model, currenti2)
plot_curr_and_resp(currenti1, V1, "Q5.d.1.png")
plot_curr_and_resp(currenti2, V2, "Q5.d.2.png")