def q2_full_full():
duration = 60
dt = 0.1
current_list1 = current_generator(duration, dt, [(7, 8), (1, 2)])
current_list2 = current_generator(duration, dt, [(1, 2), (7, 8)])
current_list3 = current_generator(duration, dt, [(1, 2), (1, 2)])
neuron_params_exc1 = {
'r': 5,
'tau': 8,
'threshold': -60,
'current': current_list1
}
neuron_params_exc2 = {
'r': 5,
'tau': 8,
'threshold': -60,
'current': current_list2
}
neuron_params_inh = {
'r': 8,
'tau': 5,
'threshold': -62,
'is_inh': True,
'current': current_list3
}
pop1 = Population(400, LIF, exc_ratio=1, **neuron_params_exc1)
pop2 = Population(200, LIF, exc_ratio=0, **neuron_params_inh)
pop3 = Population(400, LIF, exc_ratio=1, **neuron_params_exc2)
conn1 = Connection(pop1, pop1).apply("full", mu=0.2, sigma=0.1)
conn2 = Connection(pop2, pop2).apply("full", mu=0.2, sigma=0.1)
conn3 = Connection(pop3, pop3).apply("full", mu=0.2, sigma=0.1)
conn4 = Connection(pop1, pop2).apply("full", mu=0.2, sigma=0.1)
conn5 = Connection(pop2, pop3).apply("full", mu=0.2, sigma=0.1)
conn6 = Connection(pop2, pop1).apply("full", mu=0.2, sigma=0.1)
conn7 = Connection(pop3, pop2).apply("full", mu=0.2, sigma=0.1)
# conn8 = Connection(pop2, pop1).apply("full", mu=0.2, sigma=0.1)
# conn9 = Connection(pop3, pop2).apply("full", mu=0.2, sigma=0.1)
conn8 = Connection(pop3, pop1).apply("fixed_prob",
p=0.001,
mu=0.2,
sigma=0.1)
conn9 = Connection(pop1, pop3).apply("fixed_prob",
p=0.001,
mu=0.2,
sigma=0.1)
sim = Network(populations=[pop1, pop2, pop3],
connections=[
conn1, conn2, conn3, conn4, conn5, conn6, conn7, conn8,
conn9
],
time_step=dt)
sim.run(duration)
print(pop1.spikes_per_neuron.shape)
print(pop2.spikes_per_neuron.shape)
print(pop3.spikes_per_neuron.shape)
spikes = aggregate_population_spikes([pop1, pop2, pop3])
print(spikes.shape)
decision_plot(pop1.activity, pop3.activity)
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 q1_full():
duration = 20
dt = 0.1
neuron_params = {
'r': 5,
'tau': 8,
'threshold': -60,
'current': current_generator(duration, dt, [(4, 5), (4, 5)])
}
pop = Population(1000, LIF, exc_ratio=0.8, **neuron_params)
conn = Connection(pop, pop).apply("full", mu=0.2, sigma=0.1)
sim = Network(populations=[pop], connections=[conn], time_step=dt)
sim.run(duration)
print(pop.spikes_per_neuron.shape)
colors = [
'inh' if pop.neurons[int(p[0])].is_inh else 'exc'
for p in pop.spikes_per_neuron
]
raster_plot(pop.spikes_per_neuron, colors=colors)
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))
def random_current_test(Neuron, current_range, time_window, dt,
**neuron_params):
plot_name = f"./tests/phase1/{neuron_params}_{time_window}_randomCurrent.png"
lif = Neuron(current=current_generator(time_window, dt, current_range),
**neuron_params)
simulate = Network(lif, dt)
current_list, time_list = simulate.run(time_window)
plot_firing_pattern(lif.potential_list,
current_list,
time_list,
lif.u_rest,
lif.threshold,
save_to=plot_name)
def constant_current_test(Neuron, current_values, time_window, dt,
**neuron_params):
firing_patterns = []
f_i_curve_name = f"./tests/phase1/gain_function_{neuron_params}.png"
for current_value in current_values:
plot_name = f"./tests/phase1/{neuron_params}_{time_window}_{current_value}.png"
lif = Neuron(current=current_generator(time_window, dt, current_value),
**neuron_params)
simulate = Network(lif, dt)
current_list, time_list = simulate.run(time_window)
firing_patterns.append(lif.spike_times)
plot_firing_pattern(lif.potential_list,
current_list,
time_list,
lif.u_rest,
lif.threshold,
save_to=plot_name)
plot_f_i_curve(firing_patterns,
time_window,
current_values,
save_to=f_i_curve_name)
mu=1.25,
sigma=0.1,
delay=0,
**stdp_params)
conn.add([-1], [0, 1], mu=0.8, sigma=0.1, delay=0, **stdp_params)
weight_matrix = np.zeros((input_pop.size, output_pop.size))
for syn in conn.synapses:
weight_matrix[input_pop.neurons.index(syn.pre),
output_pop.neurons.index(syn.post)] = syn.w
print(weight_matrix)
sim = Network(populations=[input_pop, output_pop],
connections=[conn],
time_step=dt)
sim.run(duration, learning_rule="stdp")
for syn in conn.synapses:
weight_matrix[input_pop.neurons.index(syn.pre),
output_pop.neurons.index(syn.post)] = syn.w
# print(syn.pre.name, "--{}-->".format(syn.w), syn.post.name)
print(weight_matrix)
# for neuron in input_pop.neurons:
# print(neuron.current_list)
#
# for i in range(0, 10):
# print(
# input_pop.spikes_per_neuron[input_pop.spikes_per_neuron[:, 0] == i, 1])
#