ca.nengo.model

Interface Resettable

All Known Subinterfaces:

DecodableEnsemble, Encoder, Ensemble, ExpandableNode, ExpandableSynapticIntegrator, GolgiTendonOrgan, LinkSegmentModel, MultiTermination, MuscleSpindle, NEFEnsemble, NEFNode, Network, NeuralModule, Neuron, Node, Noise, PlasticEnsemble, PlasticNodeTermination, SkeletalMuscle, SpikeGenerator, SynapticIntegrator, Termination, ThreadTask

All Known Implementing Classes:

AbstractEnsemble, AbstractMultiTermination, AbstractNode, AdditiveGaussianExpressModel, ALIFSpikeGenerator, BasicEncoder, BasicMultiTermination, BasicOrigin, BasicTermination, BCMTermination, BiasOrigin, BiasTermination, CriticallyDampedMuscle, DecodableEnsembleImpl, DecodedOrigin, DecodedTermination, DefaultExpressModel, DefaultNeuralModule, DelayedLinearExponentialTermination, DynamicalSystemSpikeGenerator, EnsembleImpl, EnsembleTermination, ExpandableSpikingNeuron, FunctionInput, GruberNeuronFactory.GruberNeuron, GruberSpikeGenerator, HillMuscle, HodgkinHuxleySpikeGenerator, hPESTermination, IzhikevichSpikeGenerator, LearningTask, LIFSpikeGenerator, LinearExponentialTermination, LinearSynapticIntegrator, LinkSegmentModelImpl, ModulatedPlasticEnsembleTermination, NEFEnsembleImpl, NetworkArrayImpl, NetworkArrayImpl.ArrayOrigin, NetworkImpl, NetworkImpl.TerminationWrapper, NoiseFactory.NoiseImplFunction, NoiseFactory.NoiseImplNull, NoiseFactory.NoiseImplPDF, PassthroughNode, PassthroughNode.PassthroughTermination, PESTermination, PlasticEnsembleImpl, PlasticEnsembleTermination, PoissonSpikeGenerator, PreLearnTermination, ProbeTask, PyramidalNetwork, RateFunctionSpikeGenerator, SkeletalMuscleImpl, SpikingNeuron, STDPTermination, SumMultiTermination

public interface Resettable

An object that can be reset to some initial state.

Method Summary

Methods

Modifier and Type	Method and Description
void	reset(boolean randomize)

Method Detail

reset

void reset(boolean randomize)

Parameters:

randomize - True indicates that the object should be reset to a randomly selected initial state (the object must be aware of the distribution from which to draw from). False indicates that the object should be reset to a fixed initial state (which it must also know). Some objects may not support randomization of the initial state, in which case a fixed state will be used in either case.