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

      
        if(myNodeThreadPool != null){
            myNodeThreadPool.step(startTime, endTime);
        }else{
            for (Projection myProjection : myProjections) {
                InstantaneousOutput values = myProjection.getOrigin().getValues();
                myProjection.getTermination().setValues(values);
            }


            for (Node myNode : myNodes) {
                if(myNode instanceof NetworkImpl) {

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  public TimeSeries getHistory(String stateName) throws SimulationException {
    TimeSeries result = null;
    if (stateName.equals("I")) {
      result = myCurrent;
    } else if (stateName.equals("rate")) {
      InstantaneousOutput output = mySpikeOrigin.getValues();
      float[] times = myCurrent.getTimes();
      float rate = 0;
      if (output instanceof RealOutput) {
        rate = ((RealOutput) output).getValues()[0];
      } else if (output instanceof SpikeOutput) {

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    } else {
      float[] values = new float[myDimension];
      Iterator<PassthroughTermination> it = myTerminations.values().iterator();
      while (it.hasNext()) {
        PassthroughTermination termination = it.next();
        InstantaneousOutput io = termination.getValues();
        if (io instanceof RealOutput) {
          values = MU.sum(values, ((RealOutput) io).getValues());
        } else if (io instanceof SpikeOutput) {
          boolean[] spikes = ((SpikeOutput) io).getValues();
          for (int i = 0; i < spikes.length; i++) {
            if (spikes[i]) {
                            values[i] += 1f/(endTime - startTime);
                        }
          }
        } else if (io == null) {
          throw new SimulationException("Null input to Termination " + termination.getName());
        } else {
          throw new SimulationException("Output type unknown: " + io.getClass().getName());
        }
      }
      myOrigin.setValues(new RealOutputImpl(values, Units.UNK, endTime));
    }
  }

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


        //calculates rate as a function of current
        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)) {

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  /**
   * @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)) {
      float Vm = mySteadyStateVmFunction.map(new float[]{current[0]});


      float rate = 0;

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  public void testRun() throws StructuralException, SimulationException {
    myIntegrator.addTermination("test", new float[]{1}, .005f, false);
    myIntegrator.getTerminations()[0].setValues(new RealOutputImpl(new float[]{5}, Units.SPIKES_PER_S, 0));
    
    myNeuron.run(0, .005f);
    InstantaneousOutput output = myNeuron.getOrigins()[0].getValues();
    assertTrue(output instanceof SpikeOutput);
    assertTrue(((SpikeOutput) output).getValues()[0] == false);
    
    myNeuron.run(0, .005f);
    output = myNeuron.getOrigins()[0].getValues();

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    for (int i = 0; i < myNodes.length; i++) {
      myNodes[i].run(startTime, endTime);


      if (myCollectSpikesFlag && (myCollectSpikesRatio == 1 || i % myCollectSpikesRatio == 0)) {
        try {
          InstantaneousOutput output = myNodes[i].getOrigin(Neuron.AXON).getValues();
          if (output instanceof PreciseSpikeOutput) {
            PreciseSpikeOutput precise=((PreciseSpikeOutput) output);
            if (precise.getValues()[0]) {
              mySpikePattern.addSpike(i, endTime+precise.getSpikeTimes()[0]);
            }

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    }


    public void setTerminationState(float time) throws StructuralException {
        if (myLastTime >= time) { return; }


        InstantaneousOutput state = this.getInput();
        if (state == null) {
            if (myInput != null) {
                Arrays.fill(myInput, 0.0f);
            }
            if (myFilteredInput != null) {
                Arrays.fill(myFilteredInput, 0.0f);
            }
            return;
        }


        float integrationTime = 0.001f;
        if (myInput == null) {
            myInput = new float[state.getDimension()];
        }
        updateRaw(myInput, state, integrationTime);


        float tauPSC = getNodeTerminations()[0].getTau();
        if (myFilteredInput == null) {
            myFilteredInput = new float[state.getDimension()];
        }
        updateFiltered(myInput, myFilteredInput, tauPSC, integrationTime);
        myLastTime = time;
    }

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          myInSpiking[i] = false;
        }
    }


    private void updateInput() {
        InstantaneousOutput input = this.getInput();
        myInSpiking = ((SpikeOutput) input).getValues();
    }

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        updateHistory(myPostSpiking, myPostSpikeHistory, (SpikeOutput)state, time);
    }


    private void updateInput(float time) throws StructuralException {
        InstantaneousOutput input = this.getInput();


        if (!(input instanceof SpikeOutput)) {
            throw new StructuralException("Termination must be Spiking in STDPTermination");
        }

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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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