Package ca.nengo.model.impl

Examples of ca.nengo.model.impl.NodeFactory

             * Advanced properties, these may not necessarily be configued, so
            ApproximatorFactory approxFactory = (ApproximatorFactory) prop.getValue(pApproximator);
            NodeFactory nodeFactory = (NodeFactory) prop.getValue(pNodeFactory);
            Sign encodingSign = (Sign) prop.getValue(pEncodingSign);
            Float encodingDistribution = (Float) prop.getValue(pEncodingDistribution);
            Float radius = (Float) prop.getValue(pRadius);
            Float noise = (Float) prop.getValue(pNoise);

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    private void configureNodeFactory() {
        selectedItem = (ConstructableNodeFactory) factorySelector.getSelectedItem();

        try {
            NodeFactory model = (NodeFactory) ModelFactory.constructModel(selectedItem);
        } catch (ConfigException e) {
        } catch (Exception e) {
            UserMessages.showError("Could not configure Node Factory: " + e.getMessage());
View Full Code Here = name;
        this.type = type;

    protected final Object configureModel(ConfigResult configuredProperties) throws ConfigException {
        NodeFactory nodeFactory = createNodeFactory(configuredProperties);

        if (!getType().isInstance(nodeFactory)) {
            throw new ConfigException("Expected type: " + getType().getSimpleName() + " Got: "
                    + nodeFactory.getClass().getSimpleName());
        } else {
            return nodeFactory;
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     * @throws StructuralException
    private void createDendrites() throws StructuralException {
        NEFEnsemble[] e = new NEFEnsemble[this.size];
        NEFEnsembleFactoryImpl f = new NEFEnsembleFactoryImpl();
        NodeFactory g = null;
        int i = 0;
        float[][] w = MU.I(this.dim); //Identity Matrix transform for the termination on each dendritic ensemble
        Random rand = new Random(); //random number generator to select scales for dendrite trees
        Random sizeRand = new Random();//Random number generator to select subunit numbers

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    private void createTransferEnsemble() throws StructuralException
        NEFEnsemble e;
        NEFEnsembleFactoryImpl f = new NEFEnsembleFactoryImpl();
        NodeFactory g = new LIFNeuronFactory();
        int i = 0;
        int currentDim = 0;
        float[][][] w = new float[this.dim][this.dim][1];

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    private void createSoma() throws StructuralException
        NEFEnsemble e;
        NEFEnsembleFactoryImpl f = new NEFEnsembleFactoryImpl();
        NodeFactory g;

        //Originally a sigmoid function was given for the soma in the Poirazi et al. article as well.
        //This was not used due to the fact that it characterizes a rate response as opposed to spiking behaviour
        //Instead, a spiking LIFNeuron is used, until a spiking function can be found for pyramidal neurons
        //creates a standard node factory
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    NEFNode[] nodes = new NEFNode[n];

    NodeFactory nodeFactory=myEnsembleFactory.getNodeFactory();

    for (int i = 0; i < n; i++) {
      Node node = nodeFactory.make("node" + i);
      if ( !(node instanceof NEFNode) ) {
        throw new StructuralException("Nodes must be NEFNodes");
      nodes[i] = (NEFNode) node;
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        NodeFactory nodeFactory = myEnsembleFactory.getNodeFactory();
        if (nodeFactory instanceof LIFNeuronFactory) {
            LIFNeuronFactory neuronFactory = (LIFNeuronFactory)nodeFactory;

            if (!(neuronFactory.getMaxRate() instanceof IndicatorPDF) ||
                !(neuronFactory.getIntercept() instanceof IndicatorPDF)) {
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Related Classes of ca.nengo.model.impl.NodeFactory

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