"""
Simple network with a 1D population of poisson spike sources
projecting to a 2D population of IF_curr_exp neurons.
Andrew Davison, UNIC, CNRS
August 2006, November 2009
"""
import socket, os
from pyNN.utility import get_script_args
simulator_name = get_script_args(1)[0]
exec("from pyNN.%s import *" % simulator_name)
from pyNN.random import NumpyRNG
seed = 764756387
tstop = 1000.0 # ms
input_rate = 100.0 # Hz
cell_params = {'tau_refrac': 2.0, # ms
'v_thresh': -50.0, # mV
'tau_syn_E': 2.0, # ms
'tau_syn_I': 2.0} # ms
n_record = 5
node = setup(timestep=0.025, min_delay=1.0, max_delay=1.0, debug=True, quit_on_end=False)
print "Process with rank %d running on %s" % (node, socket.gethostname())
rng = NumpyRNG(seed=seed, parallel_safe=True)
print "[%d] Creating populations" % node
n_spikes = int(2*tstop*input_rate/1000.0)
spike_times = numpy.add.accumulate(rng.next(n_spikes, 'exponential',
[1000.0/input_rate], mask_local=False))
input_population = Population(2, SpikeSourceArray, {'spike_times': spike_times }, label="input")
output_population = Population(2, IF_curr_exp, cell_params, label="output")
print "[%d] input_population cells: %s" % (node, input_population.local_cells)
print "[%d] output_population cells: %s" % (node, output_population.local_cells)
print "[%d] Connecting populations" % node
connector = FixedProbabilityConnector(0.5, weights=1.0)
projection = Projection(input_population, output_population, connector, rng=rng)
file_stem = "Results/simpleRandomNetwork_np%d_%s" % (num_processes(), simulator_name)
projection.saveConnections('%s.conn' % file_stem)
input_population.record()
output_population.record()
output_population.sample(n_record, rng).record_v()
print "[%d] Running simulation" % node
run(tstop)
print "[%d] Writing spikes to disk" % node
output_population.printSpikes('%s_output.ras' % file_stem)
input_population.printSpikes('%s_input.ras' % file_stem)
print "[%d] Writing Vm to disk" % node
output_population.print_v('%s.v' % file_stem)
print "[%d] Finishing" % node
end()
print "[%d] Done" % node
