Journal of Robotics, Networking and Artificial Life
Online ISSN : 2352-6386
Print ISSN : 2405-9021
Improving Performance of Spike Pattern Detection Using Close-to-Biology Spiking Neuronal Network
Takuya Nanami Takashi Kohno
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JOURNAL OPEN ACCESS

2023 Volume 10 Issue 1 Pages 66-70

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Abstract
In the nervous system, there is a broad variety of neuron types, each exhibiting distinct firing properties. Although these neurons are considered important, the understanding of their role in information processing remains limited. In this study, we constructed a simple network using a piecewise quadratic neuron (PQN) model that can reproduce a variety of neuronal activities. Further, we examined the effect of various neuronal dynamics on the success rate of a biologically plausible spike-pattern detection task. The simulation results showed that certain mathematical structures increased the success rate of spike-pattern detection.
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© 2023 ALife Robotics Corporation Ltd.

この記事はクリエイティブ・コモンズ [表示 - 非営利 4.0 国際]ライセンスの下に提供されています。
https://creativecommons.org/licenses/by-nc/4.0/deed.ja
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