q_e = 0
s_e = 0
v_i = -75.0
h_i = 0.1
n_i = 0.1
q_i = 0
s_i = 0
initialConditions = [v_e, h_e, n_e, q_e, s_e, v_i, h_i, n_i, q_i, s_i]
t = np.arange(0, t_final, dt)
sol = odeint(lib.derivative, initialConditions, t)
v_e = sol[:, 0]
v_i = sol[:, 5]
eSpikes = lib.spikeDetection(t, v_e, -20.0)
period = eSpikes[-1] - eSpikes[-2]
print "Period of E neuron %10.3f ms" % period
pl.figure(figsize=(7, 3))
pl.plot(t, v_e, lw=2, c="r", label=r"$v_e$")
pl.plot(t, v_i, lw=2, c="b", label=r"$v_i$")
pl.xlim(min(t), max(t))
# pl.ylim(-100, 50)
pl.xlabel("time [ms]", fontsize=16)
pl.ylabel("v [mV]", fontsize=16)
pl.legend(fontsize=14, loc="upper right")
pl.xticks(range(0, int(t_final) + 1, 50))
pl.tight_layout()
pl.tick_params(labelsize=14)
m_i = m_i_inf(v_i)
h_i = h_i_inf(v_i)
n_i = n_i_inf(v_i)
q_i = np.zeros(num_i)
s_i = np.zeros(num_i)
initialConditions = np.hstack(
(v_e, h_e, n_e, q_e, s_e, v_i, h_i, n_i, q_i, s_i))
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)
