John J. M. Reynolds, James S. Plank and Catherine D. Schuman

July, 2019

IJCNN: The International Joint Conference on Neural Networks

https://www.ijcnn.org/

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Abstract

Neuromorphic Computing is a burgeoning field of research. Many groups are exploring hardware architectures and theoretical ideas about spiking recurrent neural networks. The overarching goal is to exploit the low power promise of these neuromorphic systems. However, it is difficult to train spiking recurrent neural networks (SRNNs) to perform tasks and make efficient use of neuromorphic hardware. Reservoir Computing is an attractive methodology because it requires no tuning of weights for the reservoir itself. Yet, to find optimal reservoirs, manual tuning of hyperparameters such as hidden neurons, synaptic density, and natural structure is still required. Because of this, researchers often have to generate and evaluate many networks, which can result in non-trivial amounts of computation. This paper employs the reservoir computing technique (specifically liquid state machines) and genetic algorithms in order to develop useful networks that can be deployed on neuromorphic hardware. We build on past work in reservoir computing and genetic algorithms to demonstrate the power of combining these two techniques and the advantage it can provide over manually tuning reservoirs for use on classification tasks. We discuss the complexities of determining whether or not to use the genetic algorithms approach for liquid state machine generation.

Citation Information

Text

author      J. J. M. Reynolds and J. S. Plank and C. D. Schuman
title       Intelligent Reservoir Generation for Liquid State Machines using
            Evolutionary Optimization
booktitle   IJCNN: The International Joint Conference on Neural Networks
year        2019
address     Budapest
pages       1-8
doi         10.1109/IJCNN.2019.8852472

Bibtex

@INPROCEEDINGS{rps:19:irg,
    author = "J. J. M. Reynolds and J. S. Plank and C. D. Schuman",
    title = "Intelligent Reservoir Generation for Liquid State Machines using
            Evolutionary Optimization",
    booktitle = "IJCNN: The International Joint Conference on Neural Networks",
    year = "2019",
    pages = "1-8",
    address = "Budapest",
    doi = "10.1109/IJCNN.2019.8852472"
}