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small_network.py
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small_network.py
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# encoding: utf-8
"""
Small network created with the Population and Projection classes
Usage: random_numbers.py [-h] [--plot-figure] [--debug 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 DEBUG print debugging information
"""
import numpy
from pyNN.utility import get_simulator, init_logging, normalized_filename
from pyNN.parameters import Sequence
from pyNN.random import RandomDistribution as rnd
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)
# === Define parameters ========================================================
n = 20 # Number of cells
w = 0.002 # synaptic weight (µS)
cell_params = {
'tau_m' : 20.0, # (ms)
'tau_syn_E' : 2.0, # (ms)
'tau_syn_I' : 4.0, # (ms)
'e_rev_E' : 0.0, # (mV)
'e_rev_I' : -70.0, # (mV)
'tau_refrac' : 2.0, # (ms)
'v_rest' : -60.0, # (mV)
'v_reset' : -70.0, # (mV)
'v_thresh' : -50.0, # (mV)
'cm' : 0.5} # (nF)
dt = 0.1 # (ms)
syn_delay = 1.0 # (ms)
input_rate = 50.0 # (Hz)
simtime = 1000.0 # (ms)
# === Build the network ========================================================
sim.setup(timestep=dt, max_delay=syn_delay)
cells = sim.Population(n, sim.IF_cond_alpha(**cell_params),
initial_values={'v': rnd('uniform', (-60.0, -50.0))},
label="cells")
number = int(2*simtime*input_rate/1000.0)
numpy.random.seed(26278342)
def generate_spike_times(i):
gen = lambda: Sequence(numpy.add.accumulate(numpy.random.exponential(1000.0/input_rate, size=number)))
if hasattr(i, "__len__"):
return [gen() for j in i]
else:
return gen()
assert generate_spike_times(0).max() > simtime
spike_source = sim.Population(n, sim.SpikeSourceArray(spike_times=generate_spike_times))
spike_source.record('spikes')
cells.record('spikes')
cells[0:2].record(('v', 'gsyn_exc'))
syn = sim.StaticSynapse(weight=w,delay=syn_delay)
input_conns = sim.Projection(spike_source, cells, sim.FixedProbabilityConnector(0.5), syn)
# === Run simulation ===========================================================
sim.run(simtime)
filename = normalized_filename("Results", "small_network", "pkl",
options.simulator, sim.num_processes())
cells.write_data(filename, annotations={'script_name': __file__})
print("Mean firing rate: ", cells.mean_spike_count()*1000.0/simtime, "Hz")
if options.plot_figure:
from pyNN.utility.plotting import Figure, Panel
figure_filename = filename.replace("pkl", "png")
data = cells.get_data().segments[0]
vm = data.filter(name="v")[0]
gsyn = data.filter(name="gsyn_exc")[0]
Figure(
Panel(vm, ylabel="Membrane potential (mV)"),
Panel(gsyn, ylabel="Synaptic conductance (uS)"),
Panel(data.spiketrains, xlabel="Time (ms)", xticks=True),
annotations="Simulated with %s" % options.simulator.upper()
).save(figure_filename)
print(figure_filename)
# === Clean up and quit ========================================================
sim.end()