"""
Simple network with a Poisson spike source projecting to a pair of IF_curr_alpha neurons
Andrew Davison, UNIC, CNRS
August 2006
"""
import numpy
from pyNN.utility import get_script_args
simulator_name = get_script_args(1)[0]
exec("from pyNN.%s import *" % simulator_name)
tstop = 1000.0
rate = 100.0
setup(timestep=0.1, min_delay=0.2, max_delay=1.0)
cell_params = {'tau_refrac':2.0,'v_thresh':-50.0,'tau_syn_E':2.0, 'tau_syn_I':2.0}
output_population = Population(2, IF_curr_alpha, cell_params, label="output")
number = int(2*tstop*rate/1000.0)
numpy.random.seed(26278342)
spike_times = numpy.add.accumulate(numpy.random.exponential(1000.0/rate, size=number))
assert spike_times.max() > tstop
print spike_times.min()
input_population = Population(1, SpikeSourceArray, {'spike_times': spike_times}, label="input")
projection = Projection(input_population, output_population, AllToAllConnector())
projection.setWeights(1.0)
input_population.record()
output_population.record()
output_population.record_v()
run(tstop)
output_population.printSpikes("Results/simpleNetwork_output_%s.ras" % simulator_name)
input_population.printSpikes("Results/simpleNetwork_input_%s.ras" % simulator_name)
output_population.print_v("Results/simpleNetwork_%s.v" % simulator_name)
end()
