Examples of Neuron

ca.nengo.model.neuron.Neuron

A model of a single neuron cell. Neurons are Nodes which can have multiple Terminations, and multiple Origins (corresponding to axonal output and possibly other outpus such as gap junctions), but they always have a primary Origin which is named Neuron.AXON.
@author Bryan Tripp
cnslab.cnsnetwork.Neuron
Interface for a Neuron object. @version $Date: 2012-08-04 20:43:22 +0200 (Sat, 04 Aug 2012) $ $Rev: 104 $ $Author: croft $ @author Yi Dong @author David Wallace Croft, M.Sc. @author Jeremy Cohen
com.ansj.vec.domain.Neuron
com.greentea.relaxation.jnmf.model.Neuron
Class Neuron represents neuron - the block of neural network. Neurons belonged to neuron layers. Usually (but not always) the neurons from layer k coupled with neurons from layer k+1.
com.neuralnetwork.shared.neurons.Neuron
An abstract Neuron class implements the INeuron interface. @author fredladeroute
eas.simulation.brain.neural.Neuron
@author Lukas König
javasim.Neuron
org.apache.commons.math3.ml.neuralnet.Neuron
Describes a neuron element of a neural network. This class aims to be thread-safe. @version $Id: Neuron.java 1566092 2014-02-08 18:48:29Z tn $ @since 3.3
org.dmg.pmml._40.Neuron
A representation of the model object 'Neuron'. @see org.dmg.pmml._40._40Package#getNeuron() @model abstract="true" @generated

org.neuroph.core.Neuron

 Basic general neuron model according to McCulloch-Pitts neuron model. Different neuron models can be created by using different input and transfer functions for instances of this class, or by deriving from this class. The neuron is basic processing element of neural network. This class implements the following behaviour: output = transferFunction( inputFunction(inputConnections) )

@see InputFunction @see TransferFunction @author Zoran Sevarac

tv.floe.metronome.classification.neuralnetworks.core.neurons.Neuron

Examples of org.apache.commons.math3.ml.neuralnet.Neuron

    private List<Neuron> getNeuronList() {
        // Sequence of coordinates.
        final List<Neuron> list = new ArrayList<Neuron>();


        // First neuron.
        Neuron current = net.getNeuron(FIRST_NEURON_ID);
        while (true) {
            list.add(current);
            final Collection<Neuron> neighbours
                = net.getNeighbours(current, list);

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Examples of org.apache.commons.math3.ml.neuralnet.Neuron

        final ByteArrayInputStream bis = new ByteArrayInputStream(bos.toByteArray());
        final ObjectInputStream ois = new ObjectInputStream(bis);
        final NeuronString in = (NeuronString) ois.readObject();


        for (Neuron nOut : out.getNetwork()) {
            final Neuron nIn = in.getNetwork().getNeuron(nOut.getIdentifier());


            // Same values.
            final double[] outF = nOut.getFeatures();
            final double[] inF = nIn.getFeatures();
            Assert.assertEquals(outF.length, inF.length);
            for (int i = 0; i < outF.length; i++) {
                Assert.assertEquals(outF[i], inF[i], 0d);
            }

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Examples of org.apache.commons.math3.ml.neuralnet.Neuron

        final double[] distancesBefore = new double[netSize];
        int count = 0;
        for (Neuron n : net) {
            distancesBefore[count++] = dist.compute(n.getFeatures(), features);
        }
        final Neuron bestBefore = MapUtils.findBest(features, net, dist);


        // Initial distance from the best match is larger than zero.
        Assert.assertTrue(dist.compute(bestBefore.getFeatures(), features) >= 0.2);


        update.update(net, features);


        final double[] distancesAfter = new double[netSize];
        count = 0;
        for (Neuron n : net) {
            distancesAfter[count++] = dist.compute(n.getFeatures(), features);
        }
        final Neuron bestAfter = MapUtils.findBest(features, net, dist);


        Assert.assertEquals(bestBefore, bestAfter);
        // Distance is now zero.
        Assert.assertEquals(0, dist.compute(bestAfter.getFeatures(), features), 0d);


        for (int i = 0; i < netSize; i++) {
            // All distances have decreased.
            Assert.assertTrue(distancesAfter[i] < distancesBefore[i]);
        }

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Examples of org.apache.commons.math3.ml.neuralnet.Neuron

        final ByteArrayInputStream bis = new ByteArrayInputStream(bos.toByteArray());
        final ObjectInputStream ois = new ObjectInputStream(bis);
        final NeuronString in = (NeuronString) ois.readObject();


        for (Neuron nOut : out.getNetwork()) {
            final Neuron nIn = in.getNetwork().getNeuron(nOut.getIdentifier());


            // Same values.
            final double[] outF = nOut.getFeatures();
            final double[] inF = nIn.getFeatures();
            Assert.assertEquals(outF.length, inF.length);
            for (int i = 0; i < outF.length; i++) {
                Assert.assertEquals(outF[i], inF[i], 0d);
            }

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Examples of org.apache.commons.math3.ml.neuralnet.Neuron

        final ByteArrayInputStream bis = new ByteArrayInputStream(bos.toByteArray());
        final ObjectInputStream ois = new ObjectInputStream(bis);
        final NeuronSquareMesh2D in = (NeuronSquareMesh2D) ois.readObject();


        for (Neuron nOut : out.getNetwork()) {
            final Neuron nIn = in.getNetwork().getNeuron(nOut.getIdentifier());


            // Same values.
            final double[] outF = nOut.getFeatures();
            final double[] inF = nIn.getFeatures();
            Assert.assertEquals(outF.length, inF.length);
            for (int i = 0; i < outF.length; i++) {
                Assert.assertEquals(outF[i], inF[i], 0d);
            }

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Examples of org.apache.commons.math3.ml.neuralnet.Neuron

    private List<Neuron> getNeuronList() {
        // Sequence of coordinates.
        final List<Neuron> list = new ArrayList<Neuron>();


        // First neuron.
        Neuron current = net.getNeuron(FIRST_NEURON_ID);
        while (true) {
            list.add(current);
            final Collection<Neuron> neighbours
                = net.getNeighbours(current, list);

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Examples of org.apache.commons.math3.ml.neuralnet.Neuron

     */
    public void update(Network net,
                       double[] features) {
        final long numCalls = numberOfCalls.incrementAndGet();
        final double currentLearning = learningFactor.value(numCalls);
        final Neuron best = findAndUpdateBestNeuron(net,
                                                    features,
                                                    currentLearning);


        final int currentNeighbourhood = neighbourhoodSize.value(numCalls);
        // The farther away the neighbour is from the winning neuron, the

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Examples of org.apache.commons.math3.ml.neuralnet.Neuron

     */
    private Neuron findAndUpdateBestNeuron(Network net,
                                           double[] features,
                                           double learningRate) {
        while (true) {
            final Neuron best = MapUtils.findBest(features, net, distance);


            final double[] expect = best.getFeatures();
            final double[] update = computeFeatures(expect,
                                                    features,
                                                    learningRate);
            if (best.compareAndSetFeatures(expect, update)) {
                return best;
            }


            // If another thread modified the state of the winning neuron,
            // it may not be the best match anymore for the given training

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Examples of org.apache.commons.math3.ml.neuralnet.Neuron


                default:
                    throw new MathInternalError(); // Cannot happen.
                }


                final Neuron aNeuron = network.getNeuron(identifiers[i][j]);
                for (long b : linkEnd) {
                    final Neuron bNeuron = network.getNeuron(b);
                    // Link to all neighbours.
                    // The reverse links will be added as the loop proceeds.
                    network.addLink(aNeuron, bNeuron);
                }
            }

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Examples of org.apache.commons.math3.ml.neuralnet.Neuron

     */
    public void update(Network net,
                       double[] features) {
        final long numCalls = numberOfCalls.incrementAndGet();
        final double currentLearning = learningFactor.value(numCalls);
        final Neuron best = findAndUpdateBestNeuron(net,
                                                    features,
                                                    currentLearning);


        final int currentNeighbourhood = neighbourhoodSize.value(numCalls);
        // The farther away the neighbour is from the winning neuron, the

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