def run_gj(cls, num_neu, prob=1., gjw=0.1, k_spikelet=0., Iext=12., run_duration=300):
lif = nn.LIF(num_neu)
connection = nn.conn.fixed_prob(lif.num, lif.num, prob)
if k_spikelet > 0.:
gj = cls(lif, lif, gjw, connection, delay=1., k_spikelet=k_spikelet)
else:
gj = cls(lif, lif, gjw, connection, delay=1., )
neu_mon = nn.StateMonitor(lif)
syn_mon = nn.StateMonitor(gj)
net = nn.Network(gj=gj, syn_mon=syn_mon, lif=lif, neu_mon=neu_mon)
net.run(run_duration, inputs=[lif, np.array([Iext, 0])])
fig, gs = nn.vis.get_figure(2, 1, 3, 10)
nn.vis.plot_potential(net.run_time(), neu_mon, (0, 1), fig.add_subplot(gs[0, 0]))
nn.vis.plot_value(net.run_time(), syn_mon, 'g', (0, 1), fig.add_subplot(gs[1, 0]))
plt.show()
def run_ampa(cls, num_pre=5, num_post=10, prob=1., duration=650.):
pre = nn.FreqInput(num_pre, 10., 100.)
post = nn.generate_fake_neuron(num_post)
conn = nn.conn.fixed_prob(pre.num, post.num, prob)
ampa = cls(pre, post, conn, delay=2.)
mon = nn.StateMonitor(ampa, ['g_post', 's'])
net = nn.Network(ampa, pre, post, mon)
net.run(duration, report=True)
ts = net.run_time()
fig, gs = nn.vis.get_figure(1, 1, 5, 10)
fig.add_subplot(gs[0, 0])
plt.plot(ts, mon.g_post[:, 0], label='g_post')
plt.plot(ts, mon.s[:, 0], label='s')
plt.legend()
plt.show()
def run_gaba(cls, num_pre=5, num_post=10, prob=1., monitor=[],
duration=300, stimulus_gap=10):
pre = nn.FreqInput(num_pre, 1e3 / stimulus_gap, 20.)
post = nn.generate_fake_neuron(num_post)
conn = nn.conn.fixed_prob(num_pre, num_post, prob)
gaba = cls(pre, post, conn, delay=2.)
mon = nn.StateMonitor(gaba, monitor)
net = nn.Network(gaba, pre, post, mon)
net.run(duration, report=True)
fig, gs = nn.vis.get_figure(1, 1, 5, 10)
fig.add_subplot(gs[0, 0])
for k in monitor:
plt.plot(net.run_time(), getattr(mon, k)[:, 0], label=k)
plt.legend()
plt.show()
def output_synapse(
syn_state,
var_index,
post_neu_state,
):
output_idx = var_index[-2]
syn_val = syn_state[output_idx[0]][output_idx[1]]
post_neu_state[1] += syn_val[:num_post]
post_neu_state[2] += syn_val[num_post:]
return nn.Synapses(**locals())
neurons = CUBA(num_exc + num_inh)
syn = Synapse(
neurons,
neurons,
)
mon = nn.StateMonitor(neurons, ['spike', 'spike_time'])
net = nn.Network(syn=syn, neu=neurons, mon=mon)
t0 = time.time()
net.run(10 * 1000., report=True)
print('Used time {} s.'.format(time.time() - t0))
index, time = nn.raster_plot(mon)
plt.plot(time, index, ',k')
plt.xlabel('Time (ms)')
plt.ylabel('Neuron index')
plt.show()
import npbrain as nn
nn.profile.set_backend('numba')
nn.profile.set_dt(0.02)
import matplotlib.pyplot as plt
if __name__ == '__main__':
hh = nn.HH(1, noise=1.)
mon = nn.StateMonitor(hh, ['V', 'm', 'h', 'n'])
net = nn.Network(hh=hh, mon=mon)
net.run(duration=100, inputs=[hh, 10], report=True)
ts = net.run_time()
fig, gs = nn.vis.get_figure(2, 1, 3, 12)
fig.add_subplot(gs[0, 0])
plt.plot(ts, mon.V[:, 0], label='N')
plt.ylabel('Membrane potential')
plt.xlim(-0.1, net.current_time + 0.1)
plt.legend()
fig.add_subplot(gs[1, 0])
plt.plot(ts, mon.m[:, 0], label='m')
plt.plot(ts, mon.h[:, 0], label='h')
plt.plot(ts, mon.n[:, 0], label='n')
plt.legend()
plt.xlim(-0.1, net.current_time + 0.1)
plt.xlabel('Time (ms)')
plt.show()
import matplotlib.pyplot as plt
import npbrain as nn
nn.profile.set_backend('numba')
nn.profile.set_dt(0.02)
if __name__ == '__main__':
izh = nn.Izhikevich(10, noise=1.)
mon = nn.StateMonitor(izh, ['V', 'u'])
net = nn.Network(hh=izh, mon=mon)
net.run(duration=100, inputs=[izh, 10], report=True)
ts = net.run_time()
fig, gs = nn.vis.get_figure(2, 1, 3, 12)
indexes = [0, 1, 2]
fig.add_subplot(gs[0, 0])
nn.vis.plot_potential(mon, ts, neuron_index=indexes)
plt.xlim(-0.1, net.current_time + 0.1)
plt.legend()
fig.add_subplot(gs[1, 0])
nn.vis.plot_value(mon, ts, 'u', val_index=indexes)
plt.xlim(-0.1, net.current_time + 0.1)
plt.xlabel('Time (ms)')
plt.legend()
plt.show()
import npbrain as nn
import numpy as np
import matplotlib.pyplot as plt
nn.profile.set_backend('numba')
nn.profile.set_dt(0.02)
np.random.seed(1234)
if __name__ == '__main__':
lif1 = nn.LIF(500, ref=1., noise=1.1)
lif2 = nn.LIF(1000, ref=1., noise=1.1)
conn = nn.conn.fixed_prob(lif1.num, lif2.num, prob=0.1)
syn = nn.VoltageJumpSynapse(lif1, lif2, 0.2, conn)
mon_lif1 = nn.StateMonitor(lif1)
mon2 = nn.SpikeMonitor(lif1)
mon_lif2 = nn.StateMonitor(lif2)
mon4 = nn.SpikeMonitor(lif2)
net = nn.Network(syn=syn,
lif1=lif1,
lif2=lif2,
mon1=mon_lif1,
mon2=mon2,
mon3=mon_lif2,
mon4=mon4)
net.run(duration=100, inputs=[lif1, 15], report=True)
ts = net.run_time()
fig, gs = nn.vis.get_figure(2, 1, 3, 8)
ax = fig.add_subplot(gs[0, 0])