def main(duration,
dt,
src_size,
dest_size,
interval,
initial_dopamine,
func_da,
input_pattern,
n_params,
s_params,
trace_alpha=0.1,
regularize=None,
plot_change=False):
src = InputPopulation(src_size, LIF, **n_params)
src.encode(input_pattern, duration, interval)
n_params["current"] = list(np.zeros(duration // dt))
n_params["regularize"] = regularize
dest = Population(dest_size, LIF, trace_alpha=trace_alpha, **n_params)
conn = Connection(src, dest).apply(**s_params)
net = Network(populations=[src, dest], connections=[conn], time_step=dt)
net.set_dopamine(initial_dopamine, func_da)
net.run(duration, learning_rule="rstdp")
# raster_plot(src.spikes_per_neuron)
# raster_plot(dest.spikes_per_neuron)
print(conn.weight_in_time[-1])
plot_weight_matrix(conn.weight_in_time[-1])
if plot_change:
plot_weight_change(conn.weight_in_time)
def main(trial_no,
duration,
dt,
population_size,
n,
input_size,
output_size,
interval,
initial_dopamine,
func_da,
neuron_params,
rstdp_params,
output_current=None):
inp = InputPopulation(input_size, LIF, **neuron_params)
input_pattern = []
for i in range(n):
input_pattern.append(np.random.randint(0, 5, input_size))
inp.encode(input_pattern, duration, interval, dt)
if output_current is not None:
neuron_params["current"] = output_current * np.ones(duration // dt)
outs = []
# neuron_params["regularize"] = True
for i in range(n):
outs.append(
Population(output_size, LIF, trace_alpha=0, **neuron_params))
pop = Population(population_size,
LIF,
input_part=inp,
output_part=outs,
exc_ratio=0.8,
trace_alpha=0,
**neuron_params)
conn = Connection(pop, pop, weight_change=False).apply(**rstdp_params)
net = Network(populations=[pop], connections=[conn], time_step=dt)
net.set_dopamine(initial_dopamine, func_da)
net.run(duration, learning_rule="rstdp")
inp.compute_spike_history()
raster_plot(np.array(inp.spikes_per_neuron))
activity_plot([out.activity for out in outs],
save_to="./q2trial{}.png".format(trial_no))