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"""
A demonstration of the responses of different standard neuron models to current injection.
Usage: python cell_type_demonstration.py [-h] [--plot-figure] [--debug] simulator
positional arguments:
simulator neuron, nest, brian or another backend simulator
optional arguments:
-h, --help show this help message and exit
--plot-figure Plot the simulation results to a file.
--debug Print debugging information
"""
from pyNN.utility import get_simulator, init_logging, normalized_filename
# === Configure the simulator ================================================
sim, options = get_simulator(("--plot-figure", "Plot the simulation results to a file.", {"action": "store_true"}),
("--debug", "Print debugging information"))
if options.debug:
init_logging(None, debug=True)
sim.setup(timestep=0.01, min_delay=1.0)
# === Build and instrument the network =======================================
cuba_exp = sim.Population(1, sim.IF_curr_exp(i_offset=1.0), label="IF_curr_exp")
hh = sim.Population(1, sim.HH_cond_exp(i_offset=0.2), label="HH_cond_exp")
adexp = sim.Population(1, sim.EIF_cond_exp_isfa_ista(i_offset=1.0), label="EIF_cond_exp_isfa_ista")
adapt = sim.Population(1, sim.IF_cond_exp_gsfa_grr(i_offset=2.0), label="IF_cond_exp_gsfa_grr")
izh = sim.Population(1, sim.Izhikevich(i_offset=0.01), label="Izhikevich")
all_neurons = cuba_exp + hh + adexp + adapt + izh
all_neurons.record('v')
adexp.record('w')
izh.record('u')
# === Run the simulation =====================================================
sim.run(100.0)
# === Save the results, optionally plot a figure =============================
filename = normalized_filename("Results", "cell_type_demonstration", "pkl", options.simulator)
all_neurons.write_data(filename, annotations={'script_name': __file__})
if options.plot_figure:
from pyNN.utility.plotting import Figure, Panel
figure_filename = filename.replace("pkl", "png")
Figure(
Panel(cuba_exp.get_data().segments[0].filter(name='v')[0],
ylabel="Membrane potential (mV)",
data_labels=[cuba_exp.label], yticks=True, ylim=(-66, -48)),
Panel(hh.get_data().segments[0].filter(name='v')[0],
ylabel="Membrane potential (mV)",
data_labels=[hh.label], yticks=True, ylim=(-100, 60)),
Panel(adexp.get_data().segments[0].filter(name='v')[0],
ylabel="Membrane potential (mV)",
data_labels=[adexp.label], yticks=True, ylim=(-75, -40)),
Panel(adexp.get_data().segments[0].filter(name='w')[0],
ylabel="w (nA)",
data_labels=[adexp.label], yticks=True, ylim=(0, 0.4)),
Panel(adapt.get_data().segments[0].filter(name='v')[0],
ylabel="Membrane potential (mV)",
data_labels=[adapt.label], yticks=True, ylim=(-75, -45)),
Panel(izh.get_data().segments[0].filter(name='v')[0],
ylabel="Membrane potential (mV)",
data_labels=[izh.label], yticks=True, ylim=(-80, 40)),
Panel(izh.get_data().segments[0].filter(name='u')[0],
xticks=True, xlabel="Time (ms)",
ylabel="u (mV/ms)",
data_labels=[izh.label], yticks=True, ylim=(-14, 0)),
title="Responses of standard neuron models to current injection",
annotations="Simulated with %s" % options.simulator.upper()
).save(figure_filename)
print(figure_filename)
# === Clean up and quit ========================================================
sim.end()
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