plot_spiketrains(spikes.segments[i])
plt.subplot(2, 1, 2)
plot_spiketrains(spike.segments[i])
# plt.subplot(4, 1, 3)
# plot_signal(spikes.segments[0].analogsignalarrays[0], 0)
# plt.subplot(3, 1, 3)
# plot_signal(spike.segments[1].analogsignalarrays[0], 0)
# plt.xlabel("time (%s)" % spike.segments[0].analogsignalarrays[0].times.units._dimensionality.string)
plt.setp(plt.gca().get_xticklabels(), visible=True)
# todo: check the structure of segment
plt.show()
sim.reset()
# connection.set(weight = 0.05, delay = 0.2, U = 0.5, tau_rec = 800.0, tau_facil = 0.01)
Input_E_connection.set(weight=0.05, delay=0.5)
# E_E_connection.set(weight = 0.05, delay = 0.2, U = 0.5, tau_rec = 800.0, tau_facil = 0.01)
E_E_connection.set(weight=0.05,
delay=0.2,
U=0.5,
tau_rec=800.0,
tau_facil=0.01)
print(Excinp.get('spike_times'))
stimSpikes = RandomDistribution('uniform', low=0, high=500.0).next([1, 10])
Excinp.set(spike_times=stimSpikes[0, :])
# spikes = Excinp.get_data()
# spike = Pexc.get_data()
def reset_pynn():
pynnn.reset()
def reset_pynn():
pynnn.reset()
import pyNN.brian as sim # can of course replace `nest` with `neuron`, `brian`, etc.
import matplotlib.pyplot as plt
from quantities import nA
sim.setup()
cell = sim.Population(1, sim.HH_cond_exp())
step_current = sim.DCSource(start=20.0, stop=80.0)
step_current.inject_into(cell)
cell.record('v')
for amp in (-0.2, -0.1, 0.0, 0.1, 0.2):
step_current.amplitude = amp
sim.run(100.0)
sim.reset(annotations={"amplitude": amp * nA})
data = cell.get_data()
sim.end()
for segment in data.segments:
vm = segment.analogsignals[0]
plt.plot(vm.times, vm,
label=str(segment.annotations["amplitude"]))
plt.legend(loc="upper left")
plt.xlabel("Time (%s)" % vm.times.units._dimensionality)
plt.ylabel("Membrane potential (%s)" % vm.units._dimensionality)
plt.show()