def Plot(monitor, number):
#pudb.set_trace()
br.plot(211)
if type(monitor) == list or type(monitor) == tuple:
br.plot(monitor[0].t/br.ms,monitor[0].v[0]/br.mV, label='v')
#br.plot(monitor[1].t/br.ms,monitor[1].u[0]/br.mV, label='u')
#br.plot(monitor[2].t/br.ms,monitor[0].ge[0]/br.mV, label='v')
else:
br.plot(monitor.t/br.ms,monitor.v[0]/br.mV, label='v')
#br.plot(monitor[0].t/br.ms,monitor[0].u[0]/br.mV, label='u')
#br.plot((monitor[2].t)/br.ms,(monitor[2][0]/br.mV), label='ge')
br.legend()
br.show()
def Plot(monitor, number):
#pudb.set_trace()
br.plot(211)
if type(monitor) == list or type(monitor) == tuple:
br.plot(monitor[0].t / br.ms, monitor[0].v[0] / br.mV, label='v')
#br.plot(monitor[1].t/br.ms,monitor[1].u[0]/br.mV, label='u')
#br.plot(monitor[2].t/br.ms,monitor[0].ge[0]/br.mV, label='v')
else:
br.plot(monitor.t / br.ms, monitor.v[0] / br.mV, label='v')
#br.plot(monitor[0].t/br.ms,monitor[0].u[0]/br.mV, label='u')
#br.plot((monitor[2].t)/br.ms,(monitor[2][0]/br.mV), label='ge')
br.legend()
br.show()
r0 = net['r0']
rI = net['rI']
# ri1 = net['ri1']
# ri2 = net['ri2']
b2.plot(
r1.smooth_rate(width=conv_width)[:-leaveout_steps] / b2.Hz,
r2.smooth_rate(width=conv_width)[:-leaveout_steps] / b2.Hz)
ymin, ymax = b2.ylim()
xmin, xmax = b2.xlim()
b2.ylim([min(xmin, ymin), max(xmax, ymax)])
b2.xlim([min(xmin, ymin), max(xmax, ymax)])
b2.figure()
b2.plot(r1.t[:-leaveout_steps] / b2.ms,
r1.smooth_rate(width=conv_width)[:-leaveout_steps] / b2.Hz,
label='1')
b2.plot(r2.t[:-leaveout_steps] / b2.ms,
r2.smooth_rate(width=conv_width)[:-leaveout_steps] / b2.Hz,
label='2')
b2.plot(r0.t[:-leaveout_steps] / b2.ms,
r0.smooth_rate(width=conv_width)[:-leaveout_steps] / b2.Hz,
label='0')
b2.plot(rI.t[:-leaveout_steps] / b2.ms,
rI.smooth_rate(width=conv_width)[:-leaveout_steps] / b2.Hz,
label='I')
# b2.plot(ri1.t/b2.ms, ri1.smooth_rate(width=10*b2.ms)/b2.Hz, label='i1')
# b2.plot(ri2.t/b2.ms, ri2.smooth_rate(width=10*b2.ms)/b2.Hz, label='i2')
b2.legend(title='pop.')
b2.show()
#Ni = br.NeuronGroup(3, '''dv/dt = (vt - vr)/period : volt (unless refractory)
# period: second
# fire_once: boolean''', \
# threshold='v>vt', reset='v=vr',
# refractory='fire_once')
#Ni.period = [1, 1, 7] * br.ms
#Ni.fire_once[:] = [True] * 3
indices = np.asarray([0])
times = np.asarray([6]) * br.ms
Ni = br.SpikeGeneratorGroup(3, indices=indices, times=times)
#Nh = br.NeuronGroup(1, model="""dv/dt=(gtot-v)/(10*ms) : 1
# gtot : 1""")
Nh = br.NeuronGroup(1, model="""v = I :1
I : 1""")
S = br.Synapses(Ni, Nh,
model='''tl : second
alpha=exp((tl - t)/tau1) - exp((tl - t)/tau2) : 1
w : 1
I_post = w*alpha : 1 (summed)
''',
pre='tl+=t - tl')
S.connect('True')
S.w[:, :] = '(80+rand())'
S.tl[:, :] = '0*second'
M = br.StateMonitor(Nh, 'v', record=True)
br.run(20*br.ms)
br.plot(M[0].t, M[0].v)
br.show()
r[l_idx] = la
r[r_idx] = ra
return r * b.Hz
b.defaultclock.dt = 1 * b.ms
b.prefs.codegen.target = 'numpy'
num_parts = 3
theta_max = 25.0
e = env.WormFoodEnv((2, 3), num_parts=num_parts, theta_max=theta_max)
N_i = num_parts * 4
N_h = 200
N_o = num_parts * 4
inp_eq = 'rates: Hz '
inp_th = 'rand() < rates*dt'
inp_group = b.NeuronGroup(N_i, inp_eq, threshold=inp_th)
inp_group.run_regularly('rates=inp_rates(i)', dt=b.defaultclock.dt)
M = b.StateMonitor(inp_group, 'rates', record=True)
b.run(100 * b.ms)
for ri in M.rates:
b.plot(M.t / b.ms, ri / b.Hz)
b.show()
brian_plot(connections['XeAe'].w) subplot(3,1,2) brian_plot(connections['AeAi'].w) subplot(3,1,3) brian_plot(connections['AiAe'].w) plt.figure(6) subplot(3,1,1) brian_plot(connections['XeAe'].delay) subplot(3,1,2) brian_plot(connections['AeAi'].delay) subplot(3,1,3) brian_plot(connections['AiAe'].delay) b2.ioff() b2.show()
# fire_once: boolean''', \
# threshold='v>vt', reset='v=vr',
# refractory='fire_once')
#Ni.period = [1, 1, 7] * br.ms
#Ni.fire_once[:] = [True] * 3
indices = np.asarray([0])
times = np.asarray([6]) * br.ms
Ni = br.SpikeGeneratorGroup(3, indices=indices, times=times)
#Nh = br.NeuronGroup(1, model="""dv/dt=(gtot-v)/(10*ms) : 1
# gtot : 1""")
Nh = br.NeuronGroup(1,
model="""v = I :1
I : 1""")
S = br.Synapses(Ni,
Nh,
model='''tl : second
alpha=exp((tl - t)/tau1) - exp((tl - t)/tau2) : 1
w : 1
I_post = w*alpha : 1 (summed)
''',
pre='tl+=t - tl')
S.connect('True')
S.w[:, :] = '(80+rand())'
S.tl[:, :] = '0*second'
M = br.StateMonitor(Nh, 'v', record=True)
br.run(20 * br.ms)
br.plot(M[0].t, M[0].v)
br.show()
