for ii in range(NN):
print ii
kernel = get_kernel(dt=dt, tf=tf, seed=seed+ii, number_of_processors=number_of_processors, verbose=verbose)
background_population = PoissonPopulation('bg', 600, N, kernel)
internal_population = IAFPSCDeltaPopulation('int', N, kernel, neuron_params=neuron_params)
# for curr_gid in internal_population.gids:
# kernel.SetStatus([curr_gid], {'V_m': IC_dist_data['edges'][IC_dist.rvs()]})
monitor = SpikeMonitor('int_m', internal_population, kernel)
connect_one_to_one(background_population,internal_population, .001*.03, kernel, delay=0)
connect_nsyn(internal_population, internal_population, 16, .001*.03, kernel)
kernel.Simulate(tf*1000)
t, y = monitor.firing_rate(0, tf, .005)
try:
y_tot += y
except:
y_tot = y
print time.time()-t0
plt.plot(t, y_tot/NN)
plt.show()
internal_population = IAFPSCDeltaPopulation('int',
N,
kernel,
neuron_params=neuron_params)
# for curr_gid in internal_population.gids:
# kernel.SetStatus([curr_gid], {'V_m': IC_dist_data['edges'][IC_dist.rvs()]})
monitor = SpikeMonitor('int_m', internal_population, kernel)
connect_one_to_one(background_population,
internal_population,
.001 * .03,
kernel,
delay=0)
connect_nsyn(internal_population, internal_population, 16, .001 * .03,
kernel)
kernel.Simulate(tf * 1000)
t, y = monitor.firing_rate(0, tf, .005)
try:
y_tot += y
except:
y_tot = y
print time.time() - t0
plt.plot(t, y_tot / NN)
plt.show()
kernel,
neuron_params=neuron_params)
# for curr_gid in internal_population.gids:
# kernel.SetStatus([curr_gid], {'V_m': IC_dist_data['edges'][IC_dist.rvs()]})
monitor = SpikeMonitor('int_m', internal_population, kernel)
connect_one_to_one(background_population,
internal_population,
.001 * weight_bg,
kernel,
delay=0)
connect_nsyn(internal_population,
internal_population,
nsyn_recc,
.001 * weight_recc,
kernel,
delay=delay)
kernel.Simulate(tf * 1000)
t, y = monitor.firing_rate(0, tf, .005)
try:
y_tot += y
except:
y_tot = y
print time.time() - t0
plt.plot(t, y_tot / NN)
for ii in range(NN):
print ii
kernel = get_kernel(dt=dt, tf=tf, seed=seed+ii, number_of_processors=number_of_processors, verbose=verbose)
background_population = PoissonPopulation('bg', bgfr, N, kernel)
internal_population = IAFPSCDeltaPopulation('int', N, kernel, neuron_params=neuron_params)
# for curr_gid in internal_population.gids:
# kernel.SetStatus([curr_gid], {'V_m': IC_dist_data['edges'][IC_dist.rvs()]})
monitor = SpikeMonitor('int_m', internal_population, kernel)
connect_one_to_one(background_population,internal_population, .001*weight_bg, kernel, delay=0)
connect_nsyn(internal_population, internal_population, nsyn_recc, .001*weight_recc, kernel, delay=delay)
kernel.Simulate(tf*1000)
t, y = monitor.firing_rate(0, tf, .005)
try:
y_tot += y
except:
y_tot = y
print time.time()-t0
plt.plot(t, y_tot/NN)
plt.show()