List of addBias() Examples

Examples of addBias()

ca.nengo.model.Projection.addBias()
Makes all the synaptic weights in the projection either positive or negative, so that the projection accords with Dale's principle. This introduces a bias current postsynaptically, which is a function of presynaptic activity. This bias is removed by projecting the same function through an ensemble of interneurons. See Parisien, Anderson & Eliasmith, 2007, Neural Computation for more detail. @param numInterneurons Number of interneurons through which bias function is projected @param tauInterneurons Time constant of post-synaptic current in projection from presynaptic ensemble to interneurons (typically short) @param tauBias Time constant of post-synaptic current in projection from interneurons to postsynaptic ensemble @param excitatory If true, synapses in main projection are made excitatory; if false, inhibitory @param optimize If true, performs optimizations to minimize distortion in the parallel projection through interneurons @throws StructuralException if bias can't be added

Examples of ca.nengo.model.Projection.addBias()

    network.run(-1.5f, 1);
//    Plotter.plot(probe.getData(), "mixed weights");
    float[] mixed = MU.transpose(DataUtils.filter(probe.getData(), .01f).getValues())[0];
    getError(reference, mixed);


    p.addBias(300, .005f, .01f, true, false);
    BiasOrigin bo = (BiasOrigin) pre.getOrigin("post_input");
    BiasTermination bt = (BiasTermination) post.getTermination("input (bias)");
    assertTrue(MU.min(getNetWeights(directWeights, bo, bt)) > -1e-10);
    network.run(-1.5f, 1);
//    Plotter.plot(probe.getData(), "positive non-optimal");

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Examples of ca.nengo.model.Projection.addBias()

//    float[] positiveNonOptimal = MU.transpose(DataUtils.filter(probe.getData(), .01f).getValues())[0];
//    float error = getError(reference, positiveNonOptimal);
//    assertTrue(error > 1e-10 && error < 5e-4);
    p.removeBias();


    p.addBias(300, .005f, .01f, true, true);
    bo = (BiasOrigin) pre.getOrigin("post_input");
    bt = (BiasTermination) post.getTermination("input (bias)");
    assertTrue(MU.min(getNetWeights(directWeights, bo, bt)) > -1e-10);
    network.run(-1.5f, 1);
//    Plotter.plot(probe.getData(), "positive optimal");

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Examples of ca.nengo.model.Projection.addBias()

    TimeSeries ideal = pPost.getData();
    Plotter.plot(pPost.getData(), .005f, "mixed weights result");    
    
    //remove negative weights ... 
    System.out.println("Minimum weight without bias: " + MU.min(projection.getWeights()));
    projection.addBias(100, .005f, tauPSC, true, false);
    System.out.println("Minimum weight with bias: " + MU.min(projection.getWeights()));
    pPost.reset();
    network.run(0, 2);
    TimeSeries diff = new TimeSeriesImpl(ideal.getTimes(), MU.difference(ideal.getValues(), pPost.getData().getValues()), ideal.getUnits()); 
    Plotter.plot(diff, .01f, "positive weights");

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Examples of ca.nengo.model.Projection.addBias()

    network.run(0, 2);
    TimeSeries diff = new TimeSeriesImpl(ideal.getTimes(), MU.difference(ideal.getValues(), pPost.getData().getValues()), ideal.getUnits()); 
    Plotter.plot(diff, .01f, "positive weights");
    
    projection.removeBias();
    projection.addBias(100, tauPSC/5f, tauPSC, true, true);
    pPost.reset();
    Probe pInter = network.getSimulator().addProbe("post:pre:interneurons", NEFEnsemble.X, true);
    network.run(0, 2);
    diff = new TimeSeriesImpl(ideal.getTimes(), MU.difference(ideal.getValues(), pPost.getData().getValues()), ideal.getUnits()); 
    Plotter.plot(diff, .01f, "positive weights optimized");

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