def buildModel(ind):
# model
model = nef.Network('model')
# input
inputs = model.add(routing.twoDimsRouter('inputs')); # TODO: general IO dimensions
# output
output = inFactory.makeLifNeuron(mr, 'spike', model, name='output',intercept=0);
#output = inFactory.makeLinearNeuron(mr, 'spike', model, name='output',intercept=0);
#output = inFactory.makeSigmoidNeuron(mr, 'spike', model, name='output', inflection=0.4);
CI = spiketofloat.controlledIntegrator(model,0);
outputt = model.add(routing.getFirstDim('outputt')); # TODO: general IO dimensions
model.connect(output,CI,transform=[1,0],pstc=0.1)
model.connect(CI,outputt.getTermination('in'))
##model.connect(inputs.getOrigin('out'), output, index_pre=1)
# spiking ANN
model.make('ANN', neurons=N, dimensions=INdim, mode='spike',
encoders = ([1,0], [0,1]), intercept= (ii, ii), max_rate = (mr,mr))
# wire it:
model.connect('ANN','ANN',weight_func=setReccurent); # recurrent conencitons
model.connect('ANN','output',weight_func=setOutW); # ANN to output
# for each input, make one neuron and connect it
for d in range(INdim):
# choose factory for input neurons:
#inFactory.makeLinearNeuron(mr,d,model,'spike');
#inFactory.makeBioLinearNeuron(mr,d,model,'spike');
#inFactory.makeSigmoidNeuron(mr, 'spike', model, num=d);
inFactory.makeLifNeuron(mr, 'spike', model, num=d,intercept=0);
#inFactory.makeBioSigmoidNeuron(mr,d,model,'spike');
# connect shared input to input neuron and neuron to ANN with weights
actualIn=d;
model.connect(inputs.getOrigin('out'), 'in_neuron_%d'%d, index_pre=d)
model.connect('in_neuron_%d'%d, 'ANN',weight_func=setInW)
return model;
def buildModel(ind):
# model
model = nef.Network('model')
# input
inputs = model.add(routing.twoDimsRouter('inputs')); # TODO: general IO dimensions
# output
output = inFactory.makeLifNeuron(mr, 'spike', model, name='output',intercept=0);
#output = inFactory.makeLinearNeuron(mr, 'spike', model, name='output',intercept=0);
#output = inFactory.makeSigmoidNeuron(mr, 'spike', model, name='output', inflection=0.4);
CI = spiketofloat.controlledIntegrator(model,0);
outputt = model.add(routing.getFirstDim('outputt')); # TODO: general IO dimensions
model.connect(output,CI,transform=[1,0],pstc=0.1)
model.connect(CI,outputt.getTermination('in'))
##model.connect(inputs.getOrigin('out'), output, index_pre=1)
# spiking ANN
model.make('ANN', neurons=N, dimensions=INdim, mode='spike',
encoders = ([1,0], [0,1]), intercept= (ii, ii), max_rate = (mr,mr))
# wire it:
model.connect('ANN','ANN',weight_func=setReccurent); # recurrent conencitons
model.connect('ANN','output',weight_func=setOutW); # ANN to output
# for each input, make one neuron and connect it
for d in range(INdim):
# choose factory for input neurons:
#inFactory.makeLinearNeuron(mr,d,model,'spike');
#inFactory.makeBioLinearNeuron(mr,d,model,'spike');
#inFactory.makeSigmoidNeuron(mr, 'spike', model, num=d);
inFactory.makeLifNeuron(mr, 'spike', model, num=d,intercept=0);
#inFactory.makeBioSigmoidNeuron(mr,d,model,'spike');
# connect shared input to input neuron and neuron to ANN with weights
actualIn=d;
model.connect(inputs.getOrigin('out'), 'in_neuron_%d'%d, index_pre=d)
model.connect('in_neuron_%d'%d, 'ANN',weight_func=setInW)
return model;
def buildModel(ind):
# model
model = nef.Network('model')
# input
inputs = model.add(routing.twoDimsRouter('inputs')); # TODO: general IO dimensions
# output
# output = model.make('output', neurons=OUTdim, dimensions=OUTdim, mode='spike',
# max_rate= (mr,mr), intercept=(ii,ii),encoders=[[1],[1]],decoder_sign=+1);
output = inFactory.makeLifNeuron(50, 'spike', model, name='output',intercept=0);
#output = inFactory.makeSigmoidNeuron(mr, 'spike', model, name='output');
CI = buildcontrolledIntegrator(model);
outputt = model.add(routing.getFirstDim('outputt')); # TODO: general IO dimensions
model.connect(output,CI,transform=[2,0],pstc=0.1)
model.connect(CI,outputt.getTermination('in'))
model.connect(inputs.getOrigin('out'), output, index_pre=1)
return model;
