def activation_fun(g, mu, r_plus):
val = 2.0 / np.pi * np.arctan(
g * (r_plus - mu)) * 0.5 * (np.sign(r_plus - mu) + 1.0)
return val
#generate rat data
j_vals = i_vals = np.linspace(-62.5, 62.5, 50)
for k in range(100):
for i in range(50):
for j in range(50):
i_val = i_vals[i]
j_val = j_vals[j]
r = input_neuron.input_rates(np.array([[i_val, j_val]]))
h = np.dot(last_weights[k, :], r.transpose())
firing_rate[i, j] = activation_fun(g, mu, h)
plt.matshow(np.rot90(firing_rate))
plt.colorbar()
plt.title('The final firing rate of neuron ' + str(k))
plt.axes([-62.5, 62.5, -62.5, 62.5])
# plt.xlabel(np.linspace(-62.5, 62.5, 10))
# plt.yticks(np.linspace(-62.5, 62.5, 10))
plt.savefig(PATH + 'neuron_r_' + str(k) + '.png', format="png")
plt.close()
# plt.show()
# for time_step in np.linspace(0, 100, 11):
#
last_weights = np.reshape(w_arr[-1, :], (network_size[0], 400), order="F")
def activation_fun(g, mu, r_plus):
val = 2.0 / np.pi * np.arctan(g * (r_plus - mu)) * 0.5 * (np.sign(r_plus - mu) + 1.0)
return val
# generate rat data
j_vals = i_vals = np.linspace(-62.5, 62.5, 50)
for k in range(100):
for i in range(50):
for j in range(50):
i_val = i_vals[i]
j_val = j_vals[j]
r = input_neuron.input_rates(np.array([[i_val, j_val]]))
h = np.dot(last_weights[k, :], r.transpose())
firing_rate[i, j] = activation_fun(g, mu, h)
plt.matshow(np.rot90(firing_rate))
plt.colorbar()
plt.title("The final firing rate of neuron " + str(k))
plt.axes([-62.5, 62.5, -62.5, 62.5])
# plt.xlabel(np.linspace(-62.5, 62.5, 10))
# plt.yticks(np.linspace(-62.5, 62.5, 10))
plt.savefig(PATH + "neuron_r_" + str(k) + ".png", format="png")
plt.close()
# plt.show()
# for time_step in np.linspace(0, 100, 11):
#
# -*- coding: utf-8 -*-
import input_neuron
import running_rat
print('calling running_rat')
positions = running_rat.running_rat(1e4)
print('calling input_rates')
rates = input_neuron.input_rates(positions)
input_neuron.visualize_activity(positions, rates)