Examples of ca.nengo.model.InstantaneousOutput

ca.nengo.model.InstantaneousOutput

An output from an Origin at an instant in time. This is the medium we use to pass information around a neural circuit.

Note that an Ensemble or Neuron may have multiple Origins and can therefore produce multiple outputs simultaneously. For example, one Origin of an Ensemble might produce spiking outputs, another the decoded estimates of variables it represents, and others decoded functions of these variables.

Note that the methods for getting output values from an InstantaneousOuput are not defined here, but on subinterfaces.
@author Bryan Tripp


  /**
   * @see ca.nengo.model.neuron.SpikeGenerator#run(float[], float[])
   */
  public InstantaneousOutput run(float[] time, float[] current) {
    InstantaneousOutput result = null;


    if (myMode.equals(SimulationMode.CONSTANT_RATE)) {
      result = new RealOutputImpl(new float[]{myRateFunction.map(new float[]{current[0]})}, Units.SPIKES_PER_S, time[time.length-1]);
    } else if (myMode.equals(SimulationMode.RATE)) {
      float totalTimeSpan = time[time.length-1] - time[0];

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   * 
   * @see LinearExponentialTermination#setValues(InstantaneousOutput)
   */
  public void setValues(InstantaneousOutput values) throws SimulationException {
    myQueue.add(values);
    InstantaneousOutput v = myQueue.remove(0);
    super.setValues(v);
  }

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                    if (pet instanceof ModulatedPlasticEnsembleTermination) {
                        DecodedTermination modTerm = (DecodedTermination)
                            this.getTermination(((ModulatedPlasticEnsembleTermination) pet).getModTermName());


                        InstantaneousOutput input = new RealOutputImpl(modTerm.getOutput(), Units.UNK, endTime);
                        ((ModulatedPlasticEnsembleTermination) pet).setModTerminationState
                          (modTerm.getName(), input, endTime);
                    }
                }
                catch (StructuralException e) {

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      }
      
    }


    public InstantaneousOutput getValues() throws SimulationException {
      InstantaneousOutput v0 = myOrigins[0].getValues();
      
      Units unit = v0.getUnits();
      float time = v0.getTime();
      
      if(v0 instanceof PreciseSpikeOutputImpl) {
        float[] vals = new float[myDimensions];
        int d=0;
        for(int i=0; i < myOrigins.length; i++) {

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      // TODO: Implement this? 
      return;
    }


    public InstantaneousOutput getValues() throws SimulationException {
      InstantaneousOutput v0 = myOrigins[0].getValues();
      
      Units unit = v0.getUnits();
      float time = v0.getTime();
      
      if(v0 instanceof RealOutputImpl) {
        float[] vals = new float[myOrigins[0].getDimensions()];
        for(int i = 0; i < myOrigins.length; i++) {
          float[] ovals = ((RealOutputImpl)myOrigins[i].getValues()).getValues();

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    float dN = - myN / myTauN;
    myN = Math.max(0, myN + dt*dN);


    float I = I_in - G_N*myN;


    InstantaneousOutput result = null;
    if (myMode.equals(SimulationMode.DEFAULT) || myMode.equals(SimulationMode.PRECISE)) {
      myTimeSinceLastSpike = myTimeSinceLastSpike + dt;


      float dV = (1 / myTauRC) * (I*R - myV);
      if (myTimeSinceLastSpike < myTauRef) {

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      values = myExpressModel.getOutput(startTime, state, values);
    } else {
      for (int i = 0; i < myNodes.length; i++) {
        try {
          InstantaneousOutput o = myNodes[i].getOrigin(myNodeOrigin).getValues();


          float val = 0;
          if (o instanceof SpikeOutput) {
            val = ((SpikeOutput) o).getValues()[0] ? 1f / stepSize : 0f;
          } else if (o instanceof RealOutput) {
            val = ((RealOutput) o).getValues()[0];
          } else {
            throw new Error("Node output is of type " + o.getClass().getName()
              + ". DecodedOrigin can only deal with RealOutput and SpikeOutput, so it apparently has to be updated");
          }


          float[] decoder = getDynamicDecoder(i, val, startTime, endTime);
          for (int j = 0; j < values.length; j++) {

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Related Classes of ca.nengo.model.InstantaneousOutput

ca.nengo.model.impl.AbstractEnsemble

ca.nengo.model.impl.DelayedLinearExponentialTermination

ca.nengo.model.impl.EnsembleOrigin

ca.nengo.model.impl.NetworkArrayImpl$ArrayOrigin

ca.nengo.model.impl.NetworkArrayImpl$ArraySummedOrigin

ca.nengo.model.impl.PassthroughNode

ca.nengo.model.impl.SocketUDPNode

ca.nengo.model.nef.impl.DecodedOrigin

ca.nengo.model.neuron.impl.ALIFSpikeGenerator

ca.nengo.model.neuron.impl.GruberSpikeGenerator

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