loopConnections = list()
for i in range(0, nNeurons):
singleConnection = (i, ((i + 1) % nNeurons), weight_to_spike, delay)
loopConnections.append(singleConnection)
injectionConnection = [(0, 0, weight_to_spike, 1)]
spikeArray = {'spike_times': [[0]]}
populations.append(
p.Population(nNeurons, p.IF_curr_exp, cell_params_lif, label='pop_1'))
populations.append(
p.Population(1, p.SpikeSourceArray, spikeArray, label='inputSpikes_1'))
projections.append(
p.Projection(populations[0], populations[0],
p.FromListConnector(loopConnections)))
projections.append(
p.Projection(populations[1], populations[0],
p.FromListConnector(injectionConnection)))
populations[0].record_v()
populations[0].record_gsyn()
populations[0].record(visualiser_mode=modes.RASTER)
p.run(5000)
v = None
gsyn = None
spikes = None
v = populations[0].get_v(compatible_output=True)
sim.Projection(INoisePre, pre_pop, ee_connector, target='excitatory')
sim.Projection(INoisePost, post_pop, ee_connector, target='excitatory')
# Additional Inputs projections
for i in range(len(IAddPre)):
sim.Projection(IAddPre[i], pre_pop, ee_connector, target='excitatory')
for i in range(len(IAddPost)):
sim.Projection(IAddPost[i], post_pop, ee_connector, target='excitatory')
# Plastic Connections between pre_pop and post_pop
stdp_model = sim.STDPMechanism(
timing_dependence = sim.SpikePairRule(tau_plus = 20., tau_minus = 50.0, nearest=False),
weight_dependence = sim.AdditiveWeightDependence(w_min = 0, w_max = 4.5, A_plus=0.02, A_minus = 0.02)
)
conn=sim.FromListConnector([[i,i,.05,1.0] for i in range(pop_size)])
plastic_projection = \
sim.Projection(pre_pop, post_pop, conn, #sim.FixedProbabilityConnector(p_connect=0.5,weights=0.01,delays=1.0),
synapse_dynamics = sim.SynapseDynamics(slow= stdp_model)
)
# +-------------------------------------------------------------------+
# | Simulation and results |
# +-------------------------------------------------------------------+
# Record neurons' potentials
pre_pop.record_v()
post_pop.record_v()
# Record spikes
delays = list()
loopConnections = list()
for i in range(0, nNeurons):
d_value = int(delay.uniform(low=1, high=max_delay))
delays.append(float(d_value))
singleConnection = (i, ((i + 1) % nNeurons), weight_to_spike, d_value)
loopConnections.append(singleConnection)
injectionConnection = [(0, 0, weight_to_spike, 1)]
spikeArray = {'spike_times': [[0]]}
populations.append(p.Population(nNeurons, p.IF_curr_exp, cell_params_lif, label='pop_1'))
populations.append(p.Population(1, p.SpikeSourceArray, spikeArray, label='inputSpikes_1'))
#populations[0].set_mapping_constraint({"x": 1, "y": 0})
projections.append(p.Projection(populations[0], populations[0], p.FromListConnector(loopConnections)))
projections.append(p.Projection(populations[1], populations[0], p.FromListConnector(injectionConnection)))
populations[0].record_v()
populations[0].record_gsyn()
populations[0].record(visualiser_mode=modes.RASTER)
run_time = (max_delay * nNeurons)
print "Running for {} ms".format(run_time)
p.run(run_time)
v = None
gsyn = None
spikes = None
v = populations[0].get_v(compatible_output=True)
#input_pol_2.set_mapping_constraint({'x':1,'y':0}) # this is where the sensor is going to be actually mapped in the NN
subsampled = p.Population(
subsample_size * subsample_size, # size
p.IF_curr_exp, # Neuron Type
cell_params_subsample, # Neuron Parameters
label="Input") # Label
subsampled.initialize('v', -75)
subsampled.set_mapping_constraint({'x': 0, 'y': 1})
#subsampled.record() # sends spikes to the visualiser (use parameters = 32)
p1_up = p.Projection(input_pol_1_up,
subsampled,
p.FromListConnector(
retina_lib.subSamplerConnector2D(
128, subsample_size, .2, 1)),
label='subsampling projection')
p1_down = p.Projection(input_pol_1_down,
subsampled,
p.FromListConnector(
retina_lib.subSamplerConnector2D(
128, subsample_size, .2, 1)),
label='subsampling projection')
p2_up = p.Projection(input_pol_2_up,
subsampled,
p.FromListConnector(
retina_lib.subSamplerConnector2D(
128, subsample_size, .2, 1)),
label='subsampling projection')
p2_down = p.Projection(input_pol_2_down,
p.Population(100, p.IF_curr_exp, cell_params_lif, label='pop_13'))
populations[12].set_mapping_constraint({'x': 0, 'y': 0})
populations.append(
p.Population(100, p.IF_curr_exp, cell_params_lif, label='pop_14'))
populations[13].set_mapping_constraint({'x': 0, 'y': 0})
populations.append(
p.Population(100, p.IF_curr_exp, cell_params_lif, label='pop_15'))
populations[14].set_mapping_constraint({'x': 0, 'y': 0})
populations.append(
p.Population(100, p.IF_curr_exp, cell_params_lif, label='pop_16'))
populations[15].set_mapping_constraint({'x': 4, 'y': 3})
projections.append(
p.Projection(populations[0], populations[15],
p.FromListConnector(injectionConnection)))
projections.append(
p.Projection(populations[1], populations[15],
p.FromListConnector(injectionConnection)))
projections.append(
p.Projection(populations[2], populations[15],
p.FromListConnector(injectionConnection)))
projections.append(
p.Projection(populations[3], populations[15],
p.FromListConnector(injectionConnection)))
projections.append(
p.Projection(populations[4], populations[15],
p.FromListConnector(injectionConnection)))
projections.append(
p.Projection(populations[5], populations[15],
p.FromListConnector(injectionConnection)))
'connected_chip_edge': link,
'polarity': p.ExternalFPGARetinaDevice.DOWN_POLARITY},
label='External retina'))
'''
#loopConnections = list()
#for i in range(0, 1):
# singleConnection = (i, ((i + 1) % 1), 1, 1)
# loopConnections.append(singleConnection)
populations.append(
p.Population(1024, p.IF_curr_exp, cell_params_lif, label='pop_1'))
projections.append(
p.Projection(
populations[0], populations[2],
p.FromListConnector(retina_lib.subSamplerConnector2D(128, 32, .2, 1))))
projections.append(
p.Projection(
populations[1], populations[2],
p.FromListConnector(retina_lib.subSamplerConnector2D(128, 32, .2, 1))))
populations[0].record(visualiser_mode=modes.TOPOLOGICAL,
visualiser_2d_dimension={
'x': 128,
'y': 128
})
populations[1].record(visualiser_mode=modes.TOPOLOGICAL,
visualiser_2d_dimension={
'x': 128,
'y': 128
})