Reviews of topical problems

Nonlinear dynamics and machine learning of recurrent spiking neural networks

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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.

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Fulltext is also available at DOI: 10.3367/UFNe.2021.08.039042
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
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)
BibTexBibNote ® (generic)BibNote ® (RIS) MedlineRefWorks
PT Journal Article
TI Nonlinear dynamics and machine learning of recurrent spiking neural networks
AU Maslennikov O V
FAU Maslennikov OV
AU Pugavko M M
FAU Pugavko MM
AU Shchapin D S
FAU Shchapin DS
AU Nekorkin V I
FAU Nekorkin VI
DP 10 Oct, 2022
TA Phys. Usp.
VI 65
IP 10
PG 1020-1038
RX 10.3367/UFNe.2021.08.039042
SO Phys. Usp. 2022 Oct 10;65(10):1020-1038

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