Examples of ca.nengo.model.nef.impl.DecodedTermination

ca.nengo.model.nef.impl.DecodedTermination

A Termination of decoded state vectors onto an NEFEnsemble. A DecodedTermination performs a linear transformation on incoming vectors, mapping them into the space of the NEFEnsemble to which this Termination belongs. A DecodedTermination also applies linear PSC dynamics (typically exponential decay) to the resulting vector.

Non-linear dynamics are not allowed at this level. This is because the vector input to an NEFEnsemble only has meaning in terms of the decomposition of synaptic weights into decoding vectors, transformation matrix, and encoding vectors. Linear PSC dynamics actually apply to currents, but if everything is linear we can re-order the dynamics and the encoders for convenience (so that the dynamics seem to operate on the state vectors). In contrast, non-linear dynamics must be modeled within each Neuron, because all inputs to a non-linear dynamical process must be taken into account before the effect of any single input is known.
@author Bryan Tripp

    network.addNode(post);
    post.addDecodedTermination("input", MU.I(2), .01f, false);
    Projection p = network.addProjection(pre.getOrigin(NEFEnsemble.X), post.getTermination("input"));


    DecodedOrigin o = (DecodedOrigin) pre.getOrigin(NEFEnsemble.X);
    DecodedTermination t = (DecodedTermination) post.getTermination("input");
    float[][] directWeights = MU.prod(post.getEncoders(), MU.prod(t.getTransform(), MU.transpose(o.getDecoders())));
    System.out.println("Direct weights: " + MU.min(directWeights) + " to " + MU.max(directWeights));


    Probe probe = network.getSimulator().addProbe(post.getName(), NEFEnsemble.X, true);
    network.setMode(SimulationMode.CONSTANT_RATE);
    network.run(-1.5f, 1);

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    if ( !(myOrigin instanceof DecodedOrigin) || !(myTermination instanceof DecodedTermination)) {
      throw new RuntimeException("This feature is only implemented for projections from DecodedOrigins to DecodedTerminations");
    }


    DecodedOrigin baseOrigin = (DecodedOrigin) myOrigin;
    DecodedTermination baseTermination = (DecodedTermination) myTermination;
    NEFEnsemble pre = (NEFEnsemble) baseOrigin.getNode();
    NEFEnsemble post = (NEFEnsemble) baseTermination.getNode();


    myBiasOrigin = pre.addBiasOrigin(baseOrigin, numInterneurons, getUniqueNodeName(post.getName() + "_" + baseTermination.getName()), excitatory);
    myInterneurons = myBiasOrigin.getInterneurons();
    myNetwork.addNode(myInterneurons);
    BiasTermination[] bt = post.addBiasTerminations(baseTermination, tauBias, myBiasOrigin.getDecoders(), baseOrigin.getDecoders());
    myDirectBT = bt[0];
    myIndirectBT = bt[1];
    if (!excitatory) {
            myIndirectBT.setStaticBias(new float[]{-1});
        }
    float[][] tf = new float[][]{new float[]{0, 1/tauInterneurons/tauInterneurons}, new float[]{2/tauInterneurons, 1/tauInterneurons/tauInterneurons}};
    myInterneuronTermination = (DecodedTermination) myInterneurons.addDecodedTermination("bias", MU.I(1), tf[0], tf[1], 0, false);


    myNetwork.addProjection(myBiasOrigin, myDirectBT);
    myNetwork.addProjection(myBiasOrigin, myInterneuronTermination);
    myNetwork.addProjection(myInterneurons.getOrigin(NEFEnsemble.X), myIndirectBT);


    if (optimize) {
      float[][] baseWeights = MU.prod(post.getEncoders(), MU.prod(baseTermination.getTransform(), MU.transpose(baseOrigin.getDecoders())));
      myBiasOrigin.optimizeDecoders(baseWeights, myDirectBT.getBiasEncoders(), excitatory);
      myBiasOrigin.optimizeInterneuronDomain(myInterneuronTermination, myIndirectBT);
    }


    myBiasIsEnabled = true;

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   * @see ca.nengo.model.Projection#removeBias()
   */
  public void removeBias() {
    try {
      DecodedOrigin baseOrigin = (DecodedOrigin) myOrigin;
      DecodedTermination baseTermination = (DecodedTermination) myTermination;
      NEFEnsemble pre = (NEFEnsemble) baseOrigin.getNode();
      NEFEnsemble post = (NEFEnsemble) baseTermination.getNode();


      myNetwork.removeProjection(myDirectBT);
      myNetwork.removeProjection(myIndirectBT);
      myNetwork.removeProjection(myInterneuronTermination);
      myNetwork.removeNode(myInterneurons.getName());

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      {
        terminationNodeFullName.append(tempTermination.getNode().getName() + ".");
        tempTermination = ((TerminationWrapper) tempTermination).getWrappedTermination();
      }
      
      DecodedTermination dTermination; 
      StringBuilder transformString = new StringBuilder();
      
      transformString.append('[');
      if(tempTermination instanceof DecodedTermination)
      {

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                    Origin origin = this.getOrigin(pet.getOriginName());
                    pet.setOriginState(origin.getName(), origin.getValues(), endTime);
                    pet.setTerminationState(endTime);


                    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) {
                    throw new SimulationException(e.getMessage());
                }

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ca.nengo.model.impl.ProjectionImpl

ca.nengo.model.impl.ProjectionImplTest

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