t = np.arange(0, t_final, dt)
sol = odeint(derivativePopulation, initialConditions, t)
t_e_spikes = []
t_i_spikes = []
for i in range(num_e):
ts_e = lib.spikeDetection(t, sol[:, i], spikeThreshold)
t_e_spikes.append(ts_e)
index = 5 * num_e
for i in range(index, index + num_i):
ts_i = lib.spikeDetection(t, sol[:, i], spikeThreshold)
t_i_spikes.append(ts_i)
lib.display_time(time() - start)
lib.spikeToFile(t_e_spikes, "t_e_spikes.txt")
lib.spikeToFile(t_i_spikes, "t_i_spikes.txt")
# fig, ax = pl.subplots(1, figsize=(7, 3))
# ax[0].plot(t, v, lw=2, c="k")
# ax[1].plot(t, a, lw=2, c='k')
# ax[0].set_xlim(min(t), max(t))
# ax[0].set_ylim(-100, 50)
# ax[1].set_xlabel("time [ms]")
# ax[0].set_ylabel("v [mV]")
# ax[1].set_ylabel("a [mV]")
# ax[0].set_yticks(range(-100, 100, 50))
# ax[1].set_ylim(0, 20)
# pl.tight_layout()
# pl.savefig("fig_40_3.png")
# pl.show()
lfp = np.mean(sol[:, :num_e], axis=1)
t_e_spikes = []
t_i_spikes = []
for i in range(num_e):
ts_e = lib.spikeDetection(t, sol[:, i], spikeThreshold)
t_e_spikes.append(ts_e)
index = 5 * num_e
for i in range(index, index + num_i):
ts_i = lib.spikeDetection(t, sol[:, i], spikeThreshold)
t_i_spikes.append(ts_i)
lib.display_time(time() - start)
lib.spikeToFile(t_e_spikes, "t_e_spikes1.txt")
lib.spikeToFile(t_i_spikes, "t_i_spikes1.txt")
np.savetxt("lfp1.txt", zip(t, lfp), fmt="%18.6f")
# --------------------------------------------------------------#
p_ee = 1.0 / num_e
p_ei = 1.0 / num_i
p_ie = 1.0 / num_i
p_ii = 1.0 / num_i
u_ee = rand(num_e, num_e)
u_ei = rand(num_e, num_i)
u_ie = rand(num_i, num_e)
u_ii = rand(num_i, num_i)
g_ee = (g_hat_ee * (u_ee < p_ee) / (num_e * p_ee)).T
g_ei = (g_hat_ei * (u_ei < p_ei) / (num_e * p_ei)).T
