def make_brian_group(trains):
"""Create Brian's spike generator group from spike trains.
Parameters
----------
trains : spike_trains
Input spike trains
Returns
-------
brian.SpikeGeneratorGroup
Brian's spike generator group.
"""
import brian
times = []
indices = []
for i, spikes in enumerate(trains['spikes']):
times.append(spikes)
indices.append(np.ones(len(spikes)) * i)
indices = np.concatenate(indices)
times = np.concatenate(times) * brian.second
group = brian.SpikeGeneratorGroup(
len(trains),
spiketimes=(indices, times)
)
return group
def simulation_example(brian_clock, brian):
import numpy
print "Simulation function!"
brian.defaultclock = brian_clock
Number_of_input_neurons = 20
spiketimes = []
Input_layer = brian.SpikeGeneratorGroup(20, spiketimes)
Output_layer = brian.NeuronGroup(Number_of_input_neurons,
model='v:1',
reset=0,
threshold=10)
S = brian.Synapses(Input_layer, Output_layer, model='w:1', pre='v+=w')
S[:, :] = 'i==j'
S.w = 100
return Input_layer, Output_layer, [Input_layer, Output_layer], [S], []
count = 0
g = 2
liquid_neurons = br.NeuronGroup(N_liquid[0],
model=eqs_hidden_neurons,
refractory=2 * br.ms,
reset=reset)
liquid_inputs = liquid_neurons.subgroup(N_liquid[1])
liquid_hidden = liquid_neurons.subgroup(N_liquid[0] - N_liquid[1] -
N_liquid[2])
liquid_output = liquid_neurons.subgroup(N_liquid[2])
spikes = []
hidden_neurons = [] # * len(N_hidden)
input_neurons = br.SpikeGeneratorGroup(N_in + 1, spikes)
Sin = br.Synapses(input_neurons, liquid_inputs, model='w:1', pre='ge+=w')
Sliq = br.Synapses(liquid_neurons, liquid_neurons, model='w:1', pre='ge+=w')
for i in range(len(N_hidden)):
hidden_neurons.append(
br.NeuronGroup(N_hidden[i],
model=eqs_hidden_neurons,
threshold=vt,
refractory=2 * br.ms,
reset=reset))
output_neurons = br.NeuronGroup(N_out,
model=eqs_hidden_neurons,
threshold=vt,