Reducing the computational footprint for real-time BCPNN learning

Reducing the computational footprint for real-time BCPNN learning

The implementation of synaptic plasticity in neural simulation or neuromorphic hardware is usually very resource-intensive, often requiring a compromise between efficiency and flexibility. A versatile, but computationally-expensive plasticity mechanism is provided by the Bayesian Confidence Propagat...

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Bibliographic Details
Main Authors: Bernhard eVogginger, René eSchüffny, Anders eLansner, Love eCederström, Johannes ePartzsch, Sebastian eHöppner
Format: Article
Language:English
Published: Frontiers Media S.A. 2015-01-01
Series:Frontiers in Neuroscience
Subjects:
synaptic plasticity
Hebbian Learning
spiking neural networks
look-up tables
digital neuromorphic hardware
Bayesian confidence propagation neural network (BCPNN)
Online Access:http://journal.frontiersin.org/Journal/10.3389/fnins.2015.00002/full

Internet

http://journal.frontiersin.org/Journal/10.3389/fnins.2015.00002/full

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