def show_random_neuron_in_layer(time_sampled_range,
data,
net,
layer_idx,
num_neurons=1):
THETA_D = nm.PlasticNMDASynapse.THETA_D
THETA_P = nm.PlasticNMDASynapse.THETA_P
pre_neurons = net.layers[layer_idx].nodes()
display_neurons = random.sample(pre_neurons, num_neurons)
fig, axes = plt.subplots(2, 1, sharex=True)
for (n, neuron) in enumerate(display_neurons):
ii = neuron.ii
v_m = data[:, ii]
i_inj = electrodes.sym2num(t, neuron.i_inj)
i_inj = i_inj(time_sampled_range)
axes[1].plot(time_sampled_range,
i_inj,
label=r"$I_{inj}$ Neuron %d" % neuron.ni)
axes[1].set_ylabel("I [pA]")
axes[1].legend()
axes[0].plot(time_sampled_range, v_m, label="Neuron %d" % neuron.ni)
axes[0].set_ylabel(r"$V_m$ [mV]")
axes[0].legend()
axes[-1].set_xlabel("time [ms]")
plt.suptitle("Random Neuron in layer {}".format(layer_idx))
plt.show()
def show_all_neuron_in_layer(time_sampled_range, data, net, layer_idx):
THETA_D = nm.PlasticNMDASynapse.THETA_D
THETA_P = nm.PlasticNMDASynapse.THETA_P
pre_neurons = net.layers[layer_idx].nodes()
for (n, pre_neuron) in enumerate(pre_neurons):
ii = pre_neuron.ii
v_m = data[:, ii]
ca = data[:, ii + 6]
if pre_neuron.i_inj is None:
fig, axes = plt.subplots(2, 1, sharex=True)
else:
fig, axes = plt.subplots(2, 1, sharex=True)
i_inj = electrodes.sym2num(t, pre_neuron.i_inj)
i_inj = i_inj(time_sampled_range)
axes[1].plot(time_sampled_range, i_inj, label="i_inj")
axes[1].set_ylabel("I [some unit]")
axes[1].legend()
axes[0].plot(time_sampled_range, v_m, label="V_m")
axes[0].set_ylabel("V_m [mV]")
axes[0].legend()
# axes[1].plot(time_sampled_range, ca, label="[Ca]")
# axes[1].set_ylabel(" Ca [a.u.]")
# axes[1].axhline(THETA_D, color="orange", label="theta_d")
# axes[1].axhline(THETA_P, color="green", label="theta_p")
# axes[1].legend()
axes[-1].set_xlabel("time [ms]")
plt.suptitle("Neuron {} in layer {}".format(pre_neuron.ni, layer_idx))
plt.show()
def show_one_to_one_neuron_together(time_sampled_range, data, net):
neurons = net.nodes()
fig, axes = plt.subplots(2, 1, sharex=True)
for (n, neuron) in enumerate(neurons):
ii = neuron.ii
v_m = data[:, ii]
# ca = data[:, ii+6]
i_inj = electrodes.sym2num(t, neuron.i_inj)
i_inj = i_inj(time_sampled_range)
axes[1].plot(time_sampled_range, i_inj, label="i_inj_{}".format(n))
axes[0].plot(time_sampled_range, v_m, label="V_m_{}".format(n))
axes[1].legend()
axes[0].legend()
axes[1].set_ylabel("I [some unit]")
axes[0].set_ylabel("V_m [mV]")
axes[-1].set_xlabel("time [ms]")
plt.show(block=False)
def sample_plot(time_sampled_range, data, net):
neuron_1 = list(
net.layers[0].nodes)[0] # just pick one neuron from each layer
neuron_2 = list(net.layers[1].nodes)[0]
syn = net[neuron_1][neuron_2]["synapse"]
THETA_D = syn.THETA_D
THETA_P = syn.THETA_P
for (n, neuron) in enumerate([neuron_1, neuron_2]):
ii = neuron.ii
v_m = data[:, ii]
ca = data[:, ii + 6]
i_inj = electrodes.sym2num(t, neuron.i_inj)
i_inj = i_inj(time_sampled_range)
fig, axes = plt.subplots(3, 1, sharex=True)
axes[0].plot(time_sampled_range, v_m, label="V_m")
axes[0].set_ylabel("V_m [mV]")
axes[0].legend()
axes[1].plot(time_sampled_range, ca, label="[Ca]")
axes[1].set_ylabel("Calcium [a.u.]")
axes[1].axhline(THETA_D, color="orange", label="theta_d")
axes[1].axhline(THETA_P, color="green", label="theta_p")
axes[1].legend()
axes[2].plot(time_sampled_range, i_inj, label="i_inj")
axes[2].set_ylabel(" Injected Current [some unit]")
axes[2].legend()
axes[-1].set_xlabel("time [ms]")
plt.suptitle("Neuron {}".format(n))
ii = syn.ii
plt.figure()
syn_weight = data[:, ii]
plt.plot(time_sampled_range, syn_weight, label="w_ij")
plt.xlabel("time [ms]")
plt.legend()
plt.show()