Examples of ca.nengo.model.impl.LinearExponentialTermination

ca.nengo.model.impl.LinearExponentialTermination

A Termination at which incoming spikes induce exponentially decaying post-synaptic currents that are combined linearly. Real-valued spike rate inputs have approximately the same effect over time as actual (boolean) spike inputs at the same rate.

Each input is weighted (weights specified in the constructor) so that the time integral of the post-synaptic current arising from one spike equals the weight. The time integral of post-synaptic current arising from real-valued input of 1 over a period of 1s also equals the weight. This means that spike input and spike-rate input have roughly the same effects.
@author Bryan Tripp

  }


  public Node make(String name) throws StructuralException {
    GruberSpikeGenerator generator = new GruberSpikeGenerator();
    LinearSynapticIntegrator integrator = new LinearSynapticIntegrator(.001f, Units.uAcm2);
    LinearExponentialTermination dopamineTermination
      = (LinearExponentialTermination) integrator.addTermination(DOPAMINE, new float[]{1}, .05f, true);


    float scale = myScalePDF.sample()[0];
//    float scale = 3;
    float bias = myBiasPDF.sample()[0];

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  protected void setUp() throws Exception {
    super.setUp();
    
    myNodeTerminations = new Termination[10];
    for (int i = 0; i < myNodeTerminations.length; i++) {
      myNodeTerminations[i] = new LinearExponentialTermination(null, ""+i, new float[]{1}, ourTau);
    }
    
    myEnsembleTermination = new EnsembleTermination(null, "test", myNodeTerminations);
  }

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        NEFEnsembleFactoryImpl ef = new NEFEnsembleFactoryImpl();
        NEFEnsembleImpl c = (NEFEnsembleImpl)ef.make("c", 10, 1);


        LinearExponentialTermination[] nodeterms = new LinearExponentialTermination[10];
        for(int i = 0; i < nodeterms.length; i++) {
            nodeterms[i] = new LinearExponentialTermination(new SpikingNeuron(null, null, 0.0f, 0.0f, null), null, transform[i], 0.0f);
        }


        PlasticEnsembleTermination term = new PESTermination(c, null, nodeterms);
        float[][] rettransform = term.getTransform();

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        NEFEnsembleFactoryImpl ef = new NEFEnsembleFactoryImpl();
        NEFEnsembleImpl c = (NEFEnsembleImpl)ef.make("c", 10, 1);


        LinearExponentialTermination[] nodeterms = new LinearExponentialTermination[10];
        for(int i = 0; i < nodeterms.length; i++) {
            nodeterms[i] = new LinearExponentialTermination(new SpikingNeuron(null, null, 0.0f, 0.0f, null), null, transform[i], 0.0f);
        }


        PlasticEnsembleTermination term = new PESTermination(c, null, nodeterms);
        term.setTransform(newtransform, true);

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  private static float update(Collection<LinearExponentialTermination> terminations, boolean spikes, float intTime, float decayTime) {
    float result = 0f;


    Iterator<LinearExponentialTermination> it = terminations.iterator();
    while (it.hasNext()) {
      LinearExponentialTermination t = it.next();
      float current = t.updateCurrent(spikes, intTime, decayTime);
      if (!t.getModulatory()) {
                result += current;
            }
    }


    return result;

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  public Termination addTermination(String name, float[] weights, float tauPSC, boolean modulatory) throws StructuralException {
    if (myTerminations.containsKey(name)) {
      throw new StructuralException("This SynapticIntegrator already has a Termination named " + name);
    }


    LinearExponentialTermination result = new LinearExponentialTermination(myNode, name, weights, tauPSC);
    result.setModulatory(modulatory);
    myTerminations.put(name, result);


    return result;
  }

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Related Classes of ca.nengo.model.impl.LinearExponentialTermination

ca.nengo.model.impl.EnsembleTerminationTest

ca.nengo.model.neuron.impl.GruberNeuronFactory

ca.nengo.model.neuron.impl.LinearSynapticIntegrator

ca.nengo.model.plasticity.impl.PlasticEnsembleTerminationTest

java.util.Random

ca.nengo.model.SimulationException

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