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#!/usr/bin/env python
'''
Spike-timing dependent plasticity
Adapted from Song, Miller and Abbott (2000) and Song and Abbott (2001)
This example is modified from ``synapses_STDP.py`` and writes a standalone C++ project in the directory
``STDP_standalone``.
'''
import sys, time, os
from brian2 import *
standalone = int(sys.argv[-2])
n_threads = int(sys.argv[-1])
path = 'data_stdp_%d' %n_threads
if standalone == 1:
set_device('cpp_standalone')
brian_prefs.codegen.cpp_standalone.openmp_threads = n_threads
start = time.time()
N = 1000
taum = 10 * ms
taupre = 20 * ms
taupost = taupre
Ee = 0 * mV
vt = -54 * mV
vr = -60 * mV
El = -74 * mV
taue = 5 * ms
F = 30 * Hz
gmax = .01
dApre = .01
dApost = -dApre * taupre / taupost * 1.05
dApost *= gmax
dApre *= gmax
eqs_neurons = '''
dv/dt=(ge*(Ee-vr)+El-v)/taum : volt # the synaptic current is linearized
dge/dt=-ge/taue : 1
'''
poisson_input = PoissonGroup(N, rates=F)
neurons = NeuronGroup(500, eqs_neurons, threshold='v>vt', reset='v=vr')
S = Synapses(poisson_input, neurons,
'''w:1
dApre/dt=-Apre/taupre : 1 (event-driven)
dApost/dt=-Apost/taupost : 1 (event-driven)''',
on_pre='''ge+=w
Apre+=dApre
w=clip(w+Apost,0,gmax)''',
on_post='''Apost+=dApost
w=clip(w+Apre,0,gmax)''')
S.connect()
S.w = 'rand()*gmax'
state_mon = StateMonitor(S, 'w', record=[0])
spike_mon_1 = SpikeMonitor(poisson_input)
spike_mon_2 = SpikeMonitor(neurons)
start_time = time.time()
net = Network(poisson_input, neurons, S, state_mon, spike_mon_1, spike_mon_2, name='stdp_net')
if standalone == 1:
device.insert_code('main', 'std::clock_t start = std::clock();')
net.run(5 * second, report='text')
if standalone == 1:
device.insert_code('main', '''
std::ofstream myfile ("speed.txt");
if (myfile.is_open())
{
double value = (double) (std::clock() - start)/(%d * CLOCKS_PER_SEC);
myfile << value << std::endl;
myfile.close();
}
''' %(max(1, n_threads)))
try:
os.removedirs(path)
except Exception:
pass
if standalone == 1:
device.build(project_dir=path, compile_project=True, run_project=True, debug=False)
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