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Authors: Yasuaki Kuroe 1 ; Hitoshi Iima 2 and Yutaka Maeda 3

Affiliations: 1 Faculty of Engineering Science, Kansai University, Suita-shi, Osaka, Japan, Faculty of Information and Human Sciences, Kyoto Institute of Technology, Kyoto and Japan ; 2 Faculty of Information and Human Sciences, Kyoto Institute of Technology, Kyoto and Japan ; 3 Faculty of Engineering Science, Kansai University, Suita-shi, Osaka and Japan

ISBN: 978-989-758-384-1

Keyword(s): Spiking Neural Network, Learning Method, Particle Swarm Optimization, Burst Firing, Periodic Firing.

Abstract: Recently it has been reported that artificial spiking neural networks (SNNs) are computationally more powerful than the conventional neural networks. In biological neural networks of living organisms, various firing patterns of nerve cells have been observed, typical examples of which are burst firings and periodic firings. In this paper we propose a learning method which can realize various firing patterns for recurrent SNNs (RSSNs). We have already proposed learning methods of RSNNs in which the learning problem is formulated such that the number of spikes emitted by a neuron and their firing instants coincide with given desired ones. In this paper, in addition to that, we consider several desired properties of a target RSNN and proposes cost functions for realizing them. Since the proposed cost functions are not differentiable with respect to the learning parameters, we propose a learning method based on the particle swarm optimization.

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Paper citation in several formats:
Kuroe, Y.; Iima, H. and Maeda, Y. (2019). Learning Method of Recurrent Spiking Neural Networks to Realize Various Firing Patterns using Particle Swarm Optimization.In Proceedings of the 11th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2019) ISBN 978-989-758-384-1, pages 479-486. DOI: 10.5220/0008164704790486

@conference{ncta19,
author={Yasuaki Kuroe. and Hitoshi Iima. and Yutaka Maeda.},
title={Learning Method of Recurrent Spiking Neural Networks to Realize Various Firing Patterns using Particle Swarm Optimization},
booktitle={Proceedings of the 11th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2019)},
year={2019},
pages={479-486},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008164704790486},
isbn={978-989-758-384-1},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2019)
TI - Learning Method of Recurrent Spiking Neural Networks to Realize Various Firing Patterns using Particle Swarm Optimization
SN - 978-989-758-384-1
AU - Kuroe, Y.
AU - Iima, H.
AU - Maeda, Y.
PY - 2019
SP - 479
EP - 486
DO - 10.5220/0008164704790486

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