Nonlinear dynamics and machine learning of recurrent spiking neural networks

O.V. Maslennikov^†, M.M. Pugavko, D.S. Shchapin, V.I. Nekorkin
Federal Research Center A.V. Gaponov-Grekhov Institute of Applied Physics of the Russian Academy of Sciences, ul. Ulyanova 46, Nizhny Novgorod, 603000, Russian Federation

Major achievements in designing and analyzing recurrent spiking neural networks intended for modeling functional brain networks are reviewed. Key terms and definitions employed in machine learning are introduced. The main approaches to the development and exploration of spiking and rate neural networks trained to perform specific cognitive functions are presented. State-of-the-art neuromorphic hardware systems simulating information processing by the brain are described. Concepts of nonlinear dynamics are discussed, which enable identification of the mechanisms used by neural networks to perform target tasks.

Correspondence should be addressed to
Keywords: artificial neural networks, nonlinear dynamics, machine learning, spiking neurons, modeling of cognitive functions
PACS: 07.05.Mh, 84.35.+i, 87.19.L− (all)
DOI: 10.3367/UFNe.2021.08.039042
URL: https://ufn.ru/en/articles/2022/10/b/
Web of Science

001112536300002
Scopus

2-s2.0-85182910380
ADS NASA

2022PhyU...65.1020M
Citation: Maslennikov O V, Pugavko M M, Shchapin D S, Nekorkin V I "Nonlinear dynamics and machine learning of recurrent spiking neural networks" Phys. Usp. 65 1020–1038 (2022)

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Received: 1st, June 2021, revised: 13th, August 2021, accepted: 13th, August 2021

Оригинал: Масленников О В, Пугавко М М, Щапин Д С, Некоркин В И «Нелинейная динамика и машинное обучение рекуррентных спайковых нейронных сетей» УФН 192 1089–1109 (2022); DOI: 10.3367/UFNr.2021.08.039042

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