Mapping the BCPNN Learning Rule to a Memristor Model

Mapping the BCPNN Learning Rule to a Memristor Model

The Bayesian Confidence Propagation Neural Network (BCPNN) has been implemented in a way that allows mapping to neural and synaptic processes in the human cortexandhas been used extensively in detailed spiking models of cortical associative memory function and recently also for machine learning appl...

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Detalles Bibliográficos
Main Authors: Deyu Wang, Jiawei Xu, Dimitrios Stathis, Lianhao Zhang, Feng Li, Anders Lansner, Ahmed Hemani, Yu Yang, Pawel Herman, Zhuo Zou
Formato: Artigo
Idioma:English
Publicado: Frontiers Media S.A. 2021-12-01
Series:Frontiers in Neuroscience
Subjects:
Bayesian Confidence Propagation Neural Network (BCPNN)
learning rule
memristor
nonlinear dopant drift phenomenon
synaptic state update
spiking neural networks
Acceso en liña:https://www.frontiersin.org/articles/10.3389/fnins.2021.750458/full

Internet

https://www.frontiersin.org/articles/10.3389/fnins.2021.750458/full

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