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 ca.nengo.model.neuron.Neuron

            if (getUINode(node) == null) {
                UINeoNode nodeUI = currentNodes.get(node);


                if (nodeUI == null) {
                  if (node instanceof Neuron) {
                Neuron neuron = (Neuron) node;


                UINeuron neuronUI = new UINeuron(neuron);


                addUINode(neuronUI, false, false);
              } else if (node instanceof Ensemble) {

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Examples of cnslab.cnsnetwork.Neuron

      
// TODO:  discarding first instance due known initialization bug (bug 1)      
      
new SIFNeuron ( sifNeuronPara );
      
      final Neuron  neuron = new SIFNeuron ( sifNeuronPara );
      
      // interface Neuron accessor methods
      
      // SIFNeuron is not a sensory neuron
      
      assertFalse ( neuron.isSensory ( ) );
      
      final double [ ]  currents = neuron.getCurr ( 0 );
      
      assertNotNull ( currents );
      
      // SIFNeuron has 2 currents:  excitatory and inhibitory
      
      assertEquals ( 2, currents.length );      
      
      // LOGGER.debug ( "excitatory current ...:  {}", currents [ 0 ] );
      
      // LOGGER.debug ( "inhibitory current ...:  {}", currents [ 1 ] );
      
      // Initially the currents should be zero
      
      assertEquals ( 0, currents [ 0 ], 0 );
      
      assertEquals ( 0, currents [ 1 ], 0 );
      
      final double  membraneVoltage = neuron.getMemV ( 0 );
      
      // LOGGER.debug ( "membrane voltage .....:  {}", membraneVoltage );
      
      // Initially the membrane voltage should be zero
      
      assertEquals ( 0, membraneVoltage, 0 );
      
      final boolean  record = neuron.getRecord ( );
      
      assertFalse ( record );
      
      final long  targetHost = neuron.getTHost ( );
      
      assertEquals ( 0, targetHost );
      
      final double  timeOfNextFire = neuron.getTimeOfNextFire ( );
      
      // LOGGER.debug ( "time of next fire ....:  {}", timeOfNextFire );
      
      assertEquals ( -1, timeOfNextFire, 0 );
      
      final boolean  realFire = neuron.realFire ( );
      
      assertTrue ( realFire );
    }

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Examples of com.ansj.vec.domain.Neuron

                }
                String[] strs = temp.split(" ");
                wordCount += strs.length;
                List<WordNeuron> sentence = new ArrayList<WordNeuron>();
                for (int i = 0; i < strs.length; i++) {
                    Neuron entry = wordMap.get(strs[i]);
                    if (entry == null) {
                        continue;
                    }
                    // The subsampling randomly discards frequent words while keeping the ranking same
                    if (sample > 0) {

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Examples of com.greentea.relaxation.jnmf.model.Neuron

      {
         isFirstStep = false;
         Layer lastLayer = builder.getNetwork().getLayers().getLast();
         for (int i = 0; i < targetImage.length(); ++i)
         {
            Neuron owner = lastLayer.getNeurons().get(i);
            Double value = targetImage.charAt(i) == '0' ? -1.0 : 1.0;
            owner.getOutputSynapses().get(0).sendSignal(owner, value);
//            ThresholdFunction function 
//               = (ThresholdFunction) owner.getActivationFunction();
//            function.setThresholdValue(getValue);
         }
      }

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Examples of com.greentea.relaxation.jnmf.model.Neuron

            row.add(inputColumns.get(j).getName());


            double sum = 0;
            for (int i = 0; i < outputNeuronsCount; ++i)
            {
               Neuron neuron = network.getLayers().getLast().getNeurons().get(i);


               double w = neuron.getInputSynapses().get(j).getWeight();
               sum += w;


               row.add(JNMFMathUtils.roundDouble4(w));
            }

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Examples of com.greentea.relaxation.jnmf.model.Neuron

   }


   public DoubleList resolveClusterCenter(int clusterNum)
   {
      Layer lastLayer = getNetwork().getLayers().getLast();
      Neuron clusterNeuron = lastLayer.getNeurons().get(clusterNum);


      DoubleList res = new ArrayDoubleList(clusterNeuron.getInputSynapses().size());
      for (Synapse s : clusterNeuron.getInputSynapses())
      {
         res.add(s.getWeight());
      }


      return res;

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Examples of com.greentea.relaxation.jnmf.model.Neuron

      {
         addOutputNeuron();
         return;
      }


      Neuron winner = null;
      double minF = Double.MAX_VALUE;


      for (Neuron neuron : outputNeurons)
      {
         if (neuron.getActivation() < minF)

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Examples of com.greentea.relaxation.jnmf.model.Neuron

      {
         super.afterStep();


         for (int i = 0; i < outputNeurons.size(); ++i)
         {
            Neuron neuron = outputNeurons.get(i);
            double targetValue = getCurrentLearningPair().getOutput().get(i);


            casCorMath.collectInfoFromOutput(neuron, targetValue);


            if (learningOutputsAlgorithm == LearningOutputsAlgorithm.BACK_PROPAGATION)
            {
               backPropagation.learnNeuron(neuron, targetValue);
            }
         }


         if (learningOutputsAlgorithm == LearningOutputsAlgorithm.QUICK_PROPAGATION)
         {
            quickPropagation.collectInfoFromOutputs(getCurrentLearningPair().getOutput());
         }
      }
      else if (currentState == CasCorState.CANDIDATES_COLLECT_INFO)
      {
         for (int i = 0; i < outputNeurons.size(); ++i)
         {
            Neuron neuron = outputNeurons.get(i);
            double targetValue = getCurrentLearningPair().getOutput().get(i);


            casCorMath.collectInfoFromOutput(neuron, targetValue);
         }

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Examples of com.greentea.relaxation.jnmf.model.Neuron


      double shrinkFactor = maxFactor / (1.0 + maxFactor);


      for (int i = 0; i < getOutputNeurons().size(); ++i)
      {
         Neuron neuron = getOutputNeurons().get(i);


         double connectionsCount = neuron.getInputSynapses().size();
         for (Synapse s : neuron.getInputSynapses())
         {
            double nextStep = 0;


            double slope = getSlope(s);
            double prevSlope = getPrevSlope(s);

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Examples of com.greentea.relaxation.jnmf.model.Neuron


   public void collectInfoFromOutputs(DoubleList targetValues)
   {
      for (int i = 0; i < getOutputNeurons().size(); ++i)
      {
         Neuron neuron = getOutputNeurons().get(i);


         double activation = neuron.getActivation();
         double e = calcError(targetValues.get(i), activation);
         double net = neuron.getNet();


         IFunction deriviative = neuron.getActivationFunction().getDerivative();
         double deriviativeValue = deriviative.calc(net);


         for (Synapse s : neuron.getInputSynapses())
         {
            slopes.put(s, getSlope(s) + (e * deriviativeValue * s.getSource().getActivation()));
         }
      }
   }

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