def fixed_number_pre_no_replacement(sim):
sim.setup()
p1 = sim.Population(5, sim.IF_cond_exp())
p2 = sim.Population(7, sim.IF_cond_exp())
synapse_type1 = sim.StaticSynapse(weight=0.5, delay=0.5)
connector1 = sim.FixedNumberPreConnector(n=3, with_replacement=False, rng=NumpyRNG())
prj1 = sim.Projection(p1, p2, connector1, synapse_type1)
print("Projection 1\n", connection_plot(prj1))
weights1 = prj1.get('weight', format='array', gather=False)
for column in weights1.T:
assert_equal((~numpy.isnan(column)).sum(), 3)
column[numpy.isnan(column)] = 0
assert_equal(column.sum(), 1.5)
synapse_type2 = sim.StaticSynapse(weight=RandomDistribution('gamma', k=2, theta=0.5), delay="0.2+0.3*d")
prj2 = sim.Projection(p1, p2, connector1, synapse_type2)
print("\nProjection 2\n", connection_plot(prj2))
weights2 = prj2.get('weight', format='array', gather=False)
delays2 = prj2.get('delay', format='list', gather=False)
print(weights2)
print(delays2)
for i, j, d in delays2:
assert_almost_equal(d, 0.2 + 0.3 * abs(i - j), 9)
for column in weights2.T:
assert_equal((~numpy.isnan(column)).sum(), 3)
column[numpy.isnan(column)] = 0
sim.end()
def fixed_number_pre_no_replacement(sim):
sim.setup()
p1 = sim.Population(5, sim.IF_cond_exp())
p2 = sim.Population(7, sim.IF_cond_exp())
synapse_type1 = sim.StaticSynapse(weight=0.5, delay=0.5)
connector1 = sim.FixedNumberPreConnector(n=3, with_replacement=False, rng=NumpyRNG())
prj1 = sim.Projection(p1, p2, connector1, synapse_type1)
print("Projection 1\n", connection_plot(prj1))
weights1 = prj1.get('weight', format='array', gather=False)
for column in weights1.T:
assert_equal((~numpy.isnan(column)).sum(), 3)
column[numpy.isnan(column)] = 0
assert_equal(column.sum(), 1.5)
synapse_type2 = sim.StaticSynapse(weight=RandomDistribution('gamma', k=2, theta=0.5), delay="0.2+0.3*d")
prj2 = sim.Projection(p1, p2, connector1, synapse_type2)
print("\nProjection 2\n", connection_plot(prj2))
weights2 = prj2.get('weight', format='array', gather=False)
delays2 = prj2.get('delay', format='list', gather=False)
print(weights2)
print(delays2)
for i, j, d in delays2:
assert_almost_equal(d, 0.2 + 0.3 * abs(i - j), 9)
for column in weights2.T:
assert_equal((~numpy.isnan(column)).sum(), 3)
column[numpy.isnan(column)] = 0
sim.end()
def fixed_number_post_with_replacement(sim):
sim.setup()
p1 = sim.Population(5, sim.IF_cond_exp())
p2 = sim.Population(7, sim.IF_cond_exp())
synapse_type1 = sim.StaticSynapse(weight=0.5, delay=0.5)
connector1 = sim.FixedNumberPostConnector(n=9,
with_replacement=True,
rng=NumpyRNG())
prj1 = sim.Projection(p1, p2, connector1, synapse_type1)
print("Projection #1\n", connection_plot(prj1))
delays = prj1.get('delay', format='list', gather=False)
assert_equal(len(delays), connector1.n * p1.size)
weights = prj1.get('weight', format='array', gather=False)
for row in weights:
row[numpy.isnan(row)] = 0
assert_equal(row.sum(), 4.5)
weights2 = prj1.get('weight',
format='array',
gather=False,
multiple_synapses='min')
for row in weights2:
n_nan = numpy.isnan(row).sum()
row[numpy.isnan(row)] = 0
assert_equal(row.sum(), (row.size - n_nan) * 0.5)
def fixed_number_pre_with_replacement(sim):
sim.setup()
p1 = sim.Population(5, sim.IF_cond_exp())
p2 = sim.Population(7, sim.IF_cond_exp())
synapse_type1 = sim.StaticSynapse(weight=0.5, delay=0.5)
connector1 = sim.FixedNumberPreConnector(n=3, with_replacement=True, rng=NumpyRNG())
prj1 = sim.Projection(p1, p2, connector1, synapse_type1)
print("Projection #1\n", connection_plot(prj1))
delays = prj1.get('delay', format='list', gather=False)
assert_equal(len(delays), connector1.n * p2.size)
weights = prj1.get('weight', format='array', gather=False)
for column in weights.T:
column[numpy.isnan(column)] = 0
assert_equal(column.sum(), 1.5)
def fixed_number_pre_with_replacement(sim):
sim.setup()
p1 = sim.Population(5, sim.IF_cond_exp())
p2 = sim.Population(7, sim.IF_cond_exp())
synapse_type1 = sim.StaticSynapse(weight=0.5, delay=0.5)
connector1 = sim.FixedNumberPreConnector(n=3, with_replacement=True, rng=NumpyRNG())
prj1 = sim.Projection(p1, p2, connector1, synapse_type1)
print("Projection #1\n", connection_plot(prj1))
delays = prj1.get('delay', format='list', gather=False)
assert_equal(len(delays), connector1.n * p2.size)
weights = prj1.get('weight', format='array', gather=False)
for column in weights.T:
column[numpy.isnan(column)] = 0
assert_equal(column.sum(), 1.5)
def fixed_number_post_with_replacement(sim):
sim.setup()
p1 = sim.Population(5, sim.IF_cond_exp())
p2 = sim.Population(7, sim.IF_cond_exp())
synapse_type1 = sim.StaticSynapse(weight=0.5, delay=0.5)
connector1 = sim.FixedNumberPostConnector(n=9, with_replacement=True, rng=NumpyRNG())
prj1 = sim.Projection(p1, p2, connector1, synapse_type1)
print("Projection #1\n", connection_plot(prj1))
delays = prj1.get('delay', format='list', gather=False)
assert_equal(len(delays), connector1.n * p1.size)
weights = prj1.get('weight', format='array', gather=False)
for row in weights:
row[numpy.isnan(row)] = 0
assert_equal(row.sum(), 4.5)
weights2 = prj1.get('weight', format='array', gather=False, multiple_synapses='min')
for row in weights2:
n_nan = numpy.isnan(row).sum()
row[numpy.isnan(row)] = 0
assert_equal(row.sum(), (row.size - n_nan)*0.5)
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 "tau_m =", output_population.get('tau_m')
print "[%d] Connecting populations" % node
connector = sim.FixedProbabilityConnector(0.5, rng=rng)
syn = sim.StaticSynapse(weight=1.0)
projection = sim.Projection(input_population, output_population, connector,
syn)
filename = normalized_filename("Results", "simpleRandomNetwork", "conn",
simulator_name, sim.num_processes())
projection.save('connections', filename)
print connection_plot(projection.get('weight', format='array'))
input_population.record('spikes')
output_population.record('spikes')
output_population.sample(n_record, rng).record('v')
print "[%d] Running simulation" % node
sim.run(tstop)
print "[%d] Writing spikes and Vm to disk" % node
filename = normalized_filename("Results", "simpleRandomNetwork_output", "pkl",
simulator_name, sim.num_processes())
output_population.write_data(filename, annotations={'script_name': __file__})
##input_population.write_data('%s_input.h5' % file_stem)
spike_counts = output_population.get_spike_counts()
for id in sorted(spike_counts):
input_population = sim.Population(10, sim.SpikeSourceArray(spike_times=spike_times), label="input")
output_population = sim.Population(20, sim.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 "tau_m =", output_population.get('tau_m')
print "[%d] Connecting populations" % node
connector = sim.FixedProbabilityConnector(0.5, rng=rng)
syn = sim.StaticSynapse(weight=1.0)
projection = sim.Projection(input_population, output_population, connector, syn)
filename = normalized_filename("Results", "simpleRandomNetwork", "conn",
simulator_name, sim.num_processes())
projection.save('connections', filename)
print connection_plot(projection.get('weight', format='array'))
input_population.record('spikes')
output_population.record('spikes')
output_population.sample(n_record, rng).record('v')
print "[%d] Running simulation" % node
sim.run(tstop)
print "[%d] Writing spikes and Vm to disk" % node
filename = normalized_filename("Results", "simpleRandomNetwork_output", "pkl",
simulator_name, sim.num_processes())
output_population.write_data(filename, annotations={'script_name': __file__})
##input_population.write_data('%s_input.h5' % file_stem)
spike_counts = output_population.get_spike_counts()
for id in sorted(spike_counts):
