Ejemplo n.º 1
0
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;
Ejemplo n.º 2
0
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;
Ejemplo n.º 3
0
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;