def plot_fi(duration=0.500, step_size=0.5e-3, tau_abs=0.03, tau_ref=0.010, u0=-0.065, r0=11.0, du=0.002, synapse_tau=0.010, rseed=123456, nreps=10, num_inputs=100):
np.random.seed(rseed)
#input_rates = np.arange(0.0, 150.0, 10.0)
input_rates = [0.0, 25.0, 50.0, 75.0, 100.0, 150.0]
net = SSMNetwork(step_size=step_size)
ssm = SSMNeuron()
ssm.tau_abs = tau_abs
ssm.tau_ref = tau_ref
ssm.u0 = u0
ssm.r0 = r0
ssm.du = du
ssm.synapse_tau = synapse_tau
net.add(ssm)
num_spikes = []
nsteps = int(duration / step_size)
t = np.arange(nsteps)*step_size
for irate in input_rates:
def ifunc(t):
if t > 0.020:
return irate
return 0.0
for n in range(num_inputs):
ics = PoissonCurrentStream(ifunc, step_size)
net.add_stream(ics)
net.connect_stream(ics.id, 0, ssm.id, 0.5)
net.compile()
nspikes = []
for n in range(nreps):
netcpy = copy.deepcopy(net)
ns = 0
for k,ti in enumerate(t):
ns += netcpy.state[ssm.id, -1]
netcpy.step()
nspikes.append(ns)
nspikes = np.array(nspikes)
num_spikes.append(nspikes.mean())
num_spikes = np.array(num_spikes)
firing_rate = num_spikes / duration
plt.figure()
plt.plot(input_rates, firing_rate, 'ko-')
plt.title('F/I Curve (%d neurons)' % num_inputs)
plt.xlabel('Input Rate (Hz)')
plt.ylabel('Firing Rate (Hz)')
def test_neuron(duration=0.100, step_size=0.5e-3, input_current=100.0, tau_abs=0.03, tau_ref=0.010, u0=-0.065, r0=11.0, du=0.002, synapse_tau=0.010, rseed=123456, weight=0.01):
np.random.seed(rseed)
net = SSMNetwork(step_size=step_size)
ssm = SSMNeuron()
ssm.tau_abs = tau_abs
ssm.tau_ref = tau_ref
ssm.u0 = u0
ssm.r0 = r0
ssm.du = du
ssm.synapse_tau = synapse_tau
net.add(ssm)
def ifunc(t):
if t > 0.020:
return input_current
return 0.0
ics = PoissonCurrentStream(ifunc, step_size)
net.add_stream(ics)
net.connect_stream(ics.id, 0, ssm.id, weight)
net.compile()
nsteps = int(duration / step_size)
t = np.arange(nsteps)*step_size
stime = time.time()
state = np.zeros([nsteps, 1, 5])
for k,ti in enumerate(t):
state[k, 0, :] = net.state
net.step()
etime = time.time() - stime
print 'Elapsed Time: %0.6fs' % etime
st_index = state[:, 0, 4] > 0.0
umin = -0.085
umax = 0.050
plt.figure()
plt.subplot(4, 1, 1)
u = state[:, 0, 0]
u[st_index] = umax
plt.plot(t, u, 'k-')
plt.title('u(t)')
plt.axis('tight')
plt.ylim(umin, umax)
plt.subplot(4, 1, 2)
plt.plot(t, state[:, 0, 1], 'g-')
plt.title('g(t)')
plt.axis('tight')
plt.subplot(4, 1, 3)
plt.plot(t, state[:, 0, 2], 'r-')
plt.title('R(t)')
plt.axis('tight')
plt.subplot(4, 1, 4)
p = state[:, 0, 3]
plt.plot(t, p, 'b-')
for st in t[st_index]:
plt.axvline(x=st, ymin=0.0, ymax=1.0, color='k', alpha=0.5)
plt.axis('tight')
plt.ylim(0.0, 1.0)
plt.title('p(t)')