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"""
Small network created with the Population and Projection classes
and the FromListConnector
Andrew Davison, UNIC, CNRS
April 2013
"""
import numpy as np
from pyNN.utility import get_script_args, init_logging, normalized_filename
init_logging(None, debug=True)
simulator_name = get_script_args(1)[0]
exec("from pyNN.%s import *" % simulator_name)
from pyNN.parameters import Sequence
# === Define parameters ========================================================
n = 5 # Number of cells
w = 0.5 # synaptic weight (nA)
cell_params = {
'tau_m' : 20.0, # (ms)
'tau_syn_E' : 2.0, # (ms)
'tau_syn_I' : 4.0, # (ms)
'tau_refrac' : 2.0, # (ms)
'v_rest' : 0.0, # (mV)
'v_reset' : 0.0, # (mV)
'v_thresh' : 20.0, # (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 ========================================================
setup(timestep=dt, min_delay=syn_delay, max_delay=syn_delay)
cells = Population(n, IF_curr_alpha(**cell_params), initial_values={'v': 0.0}, label="cells")
number = int(2 * simtime * input_rate / 1000.0)
np.random.seed(26278342)
def generate_spike_times(i):
gen = lambda: Sequence(np.add.accumulate(dt + np.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 = Population(n, SpikeSourceArray(spike_times=generate_spike_times))
spike_source.record('spikes')
cells.record('spikes')
cells[0:1].record('v')
connector = FromListConnector([
(0, 1, w, syn_delay),
(0, 2, w, syn_delay),
(0, 4, w, syn_delay),
(1, 0, w, syn_delay),
(1, 1, w, syn_delay),
(1, 3, w, syn_delay),
(1, 4, w, syn_delay),
(2, 3, w, syn_delay),
(3, 0, w, syn_delay),
(3, 2, w, syn_delay),
(4, 2, w, syn_delay),
])
input_conns = Projection(spike_source, cells, connector, StaticSynapse())
# === Run simulation ===========================================================
run(simtime)
#spike_source.write_data("Results/small_network_input_np%d_%s.pkl" % (num_processes(), simulator_name))
filename = normalized_filename("Results", "specific_network", "pkl",
simulator_name, num_processes())
cells.write_data(filename, annotations={'script_name': __file__})
print("Mean firing rate: ", cells.mean_spike_count() * 1000.0 / simtime, "Hz")
# === Clean up and quit ========================================================
end()
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