Necessary conditions for STDP-based pattern recognition learning in a memristive spiking neural network

This work is aimed to study experimental and theoretical approaches for searching effective local training rules for unsupervised pattern recognition by high-performance memristor-based Spiking Neural Networks (SNNs). First, the possibility of weight change using Spike-Timing-Dependent Plasticity (S...

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Veröffentlicht in:	Neural networks Jg. 134; S. 64 - 75
Hauptverfasser:	Demin, V.A., Nekhaev, D.V., Surazhevsky, I.A., Nikiruy, K.E., Emelyanov, A.V., Nikolaev, S.N., Rylkov, V.V., Kovalchuk, M.V.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Ltd 01.02.2021
Schlagworte:	Algorithms Hardware analog neuron Memristive STDP Memristor Models, Neurological Neural Networks, Computer Neuronal Plasticity - physiology Neurons - physiology Pattern Recognition, Automated - methods Probabilistic generative model Spiking neural network Unsupervised learning Hardware analog neuron Memristor Memristive STDP Probabilistic generative model Spiking neural network Unsupervised learning
ISSN:	0893-6080, 1879-2782, 1879-2782
Online-Zugang:	Volltext
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