def run_sim(ncell):
print "Cells: ", ncell
setup0 = time.time()
sim.setup(timestep=0.1)
hh_cell_type = sim.HH_cond_exp()
hh = sim.Population(ncell, hh_cell_type)
pulse = sim.DCSource(amplitude=0.5, start=20.0, stop=80.0)
pulse.inject_into(hh)
hh.record('v')
setup1 = time.time()
t0 = time.time()
sim.run(100.0)
v = hh.get_data()
sim.end()
t1 = time.time()
setup_total = setup1 - setup0
run_total = t1 - t0
print "Setup: ", setup_total
print "Run: ", run_total
print "Total sim time: ", setup_total + run_total
return run_total
import pyNN.neuron 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()