Research on Sparsity of Output Synapses in Echo State Networks

This paper presents an improved model of echo state networks (ESNs) and gives the definitions of energy consumption, energy efficiency, etc. We verify the existence of redundant output synaptic connections by numerical simulations. We investigate the relationships among energy consumption, predictio...

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Bibliographic Details
Published in:Mathematical problems in engineering 2018-01, Vol.2018 (2018), p.1-12
Main Authors: Mu, Xiaohui, He, Xiangyu, Li, Lixiang
Format: Article
Language:English
Subjects:
Artificial intelligence
Brain research
Computer simulation
Energy consumption
Energy efficiency
Information processing
Mathematical models
Metabolism
Neural networks
Neurons
Performance prediction
Power efficiency
Redundancy
Sparsity
Synapses
Time series
Wireless networks
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Summary:This paper presents an improved model of echo state networks (ESNs) and gives the definitions of energy consumption, energy efficiency, etc. We verify the existence of redundant output synaptic connections by numerical simulations. We investigate the relationships among energy consumption, prediction step, and the sparsity of ESN. At the same time, the energy efficiency and the prediction steps are found to present the same variation trend when silencing different synapses. Thus, we propose a computationally efficient method to locate redundant output synapses based on energy efficiency of ESN. We find that the neuron states of redundant synapses can be linearly represented by the states of other neurons. We investigate the contributions of redundant and core output synapses to the performance of network prediction. For the prediction task of chaotic time series, the predictive performance of ESN is improved about hundreds of steps by silencing redundant synapses.
ISSN:1024-123X
1563-5147
DOI:10.1155/2018/1984524