Optimizing BCPNN Learning Rule for Memory Access

Optimizing BCPNN Learning Rule for Memory Access

Simulation of large scale biologically plausible spiking neural networks, e.g., Bayesian Confidence Propagation Neural Network (BCPNN), usually requires high-performance supercomputers with dedicated accelerators, such as GPUs, FPGAs, or even Application-Specific Integrated Circuits (ASICs). Almost...

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Bibliographic Details
Main Authors: Yu Yang, Dimitrios Stathis, Rodolfo Jordão, Ahmed Hemani, Anders Lansner
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-08-01
Series:Frontiers in Neuroscience
Subjects:
Bayesian Confidence Propagation Neural Network (BCPNN)
neuromorphic computing
Hebbian learning
spiking neural networks
memory optimization
DRAM
Online Access:https://www.frontiersin.org/article/10.3389/fnins.2020.00878/full

Internet

https://www.frontiersin.org/article/10.3389/fnins.2020.00878/full

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