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RANDOM NEURAL NETWORK METHODS AND DEEP LEARNING

Published online by Cambridge University Press:  30 January 2019

Yonghua Yin Open the ORCID record for Yonghua Yin [Opens in a new window]
Yonghua Yin*
Affiliation:
Intelligent Systems and Networks Group Department of Electrical and Electronic Engineering Imperial College, London SW7 2BT, UK E-mail: y.yin14@imperial.ac.uk
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Abstract

The random neural network (RNN) is a mathematical model for an “integrate and fire” spiking network that closely resembles the stochastic behavior of neurons in mammalian brains. Since its proposal in 1989, there have been numerous investigations into the RNN's applications and learning algorithms. Deep learning (DL) has achieved great success in machine learning. Recently, the properties of the RNN for DL have been investigated, in order to combine their power. Recent results demonstrate that the gap between RNNs and DL can be bridged and the DL tools based on the RNN are faster and can potentially be used with less energy expenditure than existing methods.

Keywords

computational probabilityqueueing theorystochastic modeling

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Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 35 , Special Issue 1: Learning, Optimization, and Theory of G-Networks , January 2021 , pp. 6 - 36
Copyright
Copyright © Cambridge University Press 2019

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