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Nonlinear dynamics and machine learning of recurrent spiking neural networks

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Federal Research Center Institute of Applied Physics of the Russian Academy of Sciences, ul. Ulyanova 46, Nizhny Novgorod, 603000, Russian Federation

The review describes the main results in the field of design and analysis of recurrent spiking neural networks for modeling functional brain networks. Key terms and definitions from the field of machine learning are given. The main approaches to the construction and study of spiking and rate neural networks trained to perform specific cognitive functions are shown. The modern hardware neuromorphic systems that imitate the information processing by the brain are described. The principles of nonlinear dynamics are discussed, which make it possible to identify the mechanisms for performing target tasks by neural networks.

Keywords: artificial neural networks, nonlinear dynamics, machine learning, spiking neurons, modeling of cognitive functions
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., accepted

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, принята к публикации; DOI: 10.3367/UFNr.2021.08.039042

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