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• W1994918405 abstract "Spiking neural networks (SNN) are promising artificial neural network (ANN) models as they utilise information representation as trains of spikes, that adds new dimensions of time, frequency and phase to the structure and the functionality of ANN. The current SNN models though are deterministic, that restricts their applications for large scale engineering and cognitive modelling of stochastic processes. This paper proposes a novel probabilistic spiking neuron model (pSNM) and suggests ways of building pSNN for a wide range of applications including classification, string pattern recognition and associative memory. It also extends previously published computational neurogenetic models." @default.
• W1994918405 created "2016-06-24" @default.
• W1994918405 creator A5024893012 @default.
• W1994918405 date "2010-01-01" @default.
• W1994918405 modified "2023-10-16" @default.
• W1994918405 title "To spike or not to spike: A probabilistic spiking neuron model" @default.
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• W1994918405 doi "https://doi.org/10.1016/j.neunet.2009.08.010" @default.
• W1994918405 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/19783402" @default.
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