About: Spiking neural network

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Spiking neural networks (SNNs) are artificial neural networks that more closely mimic natural neural networks. In addition to neuronal and synaptic state, SNNs incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not transmit information at each propagation cycle (as it happens with typical multi-layer perceptron networks), but rather transmit information only when a membrane potential – an intrinsic quality of the neuron related to its membrane electrical charge – reaches a specific value, called the threshold. When the membrane potential reaches the threshold, the neuron fires, and generates a signal that travels to other neurons which, in turn, increase or decrease their potentials in response to this signal. A neuron model that fires at the

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rdf:type	Thing yago:WikicatArtificialNeuralNetworks yago:WikicatNeuralNetworks yago:Abstraction100002137 yago:Communication100033020 yago:ComputerArchitecture106725249 yago:Description106724763 yago:Message106598915 yago:NeuralNetwork106725467 yago:Specification106725067 yago:Statement106722453
rdfs:label	Gepulste neuronale Netze (de) Red neuronal de impulsos (es) Réseau de neurones à impulsions (fr) Rete neurale spiking (it) Spiking neural network (en) Импульсная нейронная сеть (ru)
rdfs:comment	Las redes neuronales de impulsos (en inglés: spiking neural networks) son un tipo de redes neuronales artificiales más realistas que las redes neuronales artificiales clásicas, es decir, procesan la información de una forma más similar a las redes neuronales biológicas. (es) Импульсная нейронная сеть (ИмНС, англ. Pulsed neural networks, PNN) или Спайковая нейронная сеть (СНН, англ. Spiking neural network, SNN) — третье поколение искусственных нейронных сетей (ИНС), которое отличается от бинарных (первое поколение) и частотных/скоростных (второе поколение) ИНС тем, что в нем нейроны обмениваются короткими (у биологических нейронов — около 1—2 мс) импульсами одинаковой амплитуды (у биологических нейронов — около 100 мВ).Является самой реалистичной, с точки зрения физиологии, моделью ИНС. (ru) Gepulste neuronale Netze (kurz: SNN, englisch: Spiking neural networks) sind eine Variante künstlicher neuronaler Netzwerke, die näher an biologischen neuronalen Netzen sind als beispielsweise das mehrlagige Perzeptron. Gepulste neuronale Netze werden auch als Netze der dritten Generation bezeichnet. Aus Sicht der Informationstheorie ist ein Modell gesucht, das erklärt, wie Informationen durch Pulse codiert und decodiert werden. So ist beispielsweise nicht abschließend geklärt, ob die Informationen durch die Feuerrate oder durch eine zeitliche Codierung übertragen werden. (de) Les réseaux de neurones à impulsions (SNN: Spike Neural Networks, en anglais) sont un raffinement des réseaux de neurones artificiels (ANN: Artificial Neural Network, en anglais) où l’échange entre neurones repose sur l’intégration des impulsions et la redescente de l’activation, à l’instar des neurones naturels. L’encodage est donc temporel et binaire. Ces inconvénients sont aussi des avantages dans une perspective spatio-temporelle : l’intégration limite l’activation aux neurones voisins (espace) et tolère la perte d’information (temps). (fr) Spiking neural networks (SNNs) are artificial neural networks that more closely mimic natural neural networks. In addition to neuronal and synaptic state, SNNs incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not transmit information at each propagation cycle (as it happens with typical multi-layer perceptron networks), but rather transmit information only when a membrane potential – an intrinsic quality of the neuron related to its membrane electrical charge – reaches a specific value, called the threshold. When the membrane potential reaches the threshold, the neuron fires, and generates a signal that travels to other neurons which, in turn, increase or decrease their potentials in response to this signal. A neuron model that fires at the (en) Una rete neurale spiking, in sigla SNN (dall'inglese spiking neural network), è una rete neurale artificiale a impulso che tenta di mimare più realmente le reti neurali naturali. Oltre allo stato sinaptico e neuronale una rete di questo tipo incorpora anche il concetto di tempo nel suo modello operativo. L'idea è che i neuroni artificiali non attivino in automatico ognuno un ciclo di propagazione come nelle reti multistrato con percettrone, ma piuttosto quando un potenziale di membrana - una intrinseca qualità del neurone correlata alla carica della sua membrana elettrica - raggiunge uno specifico valore. Quando un neurone si attiva genera un segnale che viaggia verso altri neuroni, che a turno incrementano o decrementano i loro potenziali in accordo a questo segnale. (it)
rdfs:seeAlso	Biological neuron model
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dcterms:subject	Computational statistics Articles containing video clips Computational neuroscience Artificial neural networks
Wikipage page ID	10159567 (xsd:integer)
Wikipage revision ID	1124973708 (xsd:integer)
Link from a Wikipage to another Wikipage	Caltech PyTorch Python (programming language) Electrical synapse Electrophysiology TrueNorth Biological neuron model Brian (software) Computational statistics Application software Human Brain Project Perceptron Unity (game engine) University of California, Santa Cruz University of Manchester University of Massachusetts Amherst Deep learning Network topology Neurotransmitter Articles containing video clips Massively parallel computing Membrane potential dbr:Norse_(neuron_simulator) Neuroinformatics École Normale Supérieure Operating Model GENESIS (software) Gradient descent NEST (software) Convolutional neural network Computational neuroscience SpiNNaker Synapse Backpropagation Computational neuroscience C++ C Sharp (programming language) Action potential Hebbian theory Lasso (statistics) Leaky integrator Neuroethology Neuromorphic engineering ARM architecture Duke University Central nervous system Biological neural network Differential equation Hindmarsh–Rose model Recurrent neural network Heidelberg University Attractor network Artificial neural network Artificial neural networks KTH Royal Institute of Technology CoDi Cognitive architecture Cognitive computer Cognitive map Echo state network Hodgkin–Huxley model Models of neural computation Spike-timing-dependent plasticity Artificial neuron

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