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

References (162) Cited by (16) Similar articles (20) ↓

A.S. Ilin, A.N. Matsukatova et al “Memristors for neuromorphic computing systems: basic parameters and methods of their optimization” Phys. Usp. 69 344–356 (2026)
M.I. Rabinovich, M.K. Muezzinoglu “Nonlinear dynamics of the brain: emotion and cognition” Phys. Usp. 53 357–372 (2010)
A.N. Pisarchik, A.E. Hramov “Stochastic processes in the brain's neural network and their impact on perception and decision-making” Phys. Usp. 66 1224–1247 (2023)
A.N. Pavlov, A.E. Hramov et al “Wavelet analysis in neurodynamics” Phys. Usp. 55 845–875 (2012)
A.I. Musorin, A.S. Shorokhov et al “Photonics approaches to the implementation of neuromorphic computing” Phys. Usp. 66 1211–1223 (2023)
A.E. Hramov, N.S. Frolov et al “Functional networks of the brain: from connectivity restoration to dynamic integration” Phys. Usp. 64 584–616 (2021)
O.V. Maslennikov, V.I. Nekorkin “Adaptive dynamical networks” Phys. Usp. 60 694–704 (2017)
V.V. Klinshov, V.I. Nekorkin “Synchronization of delay-coupled oscillator networks” Phys. Usp. 56 1217–1229 (2013)
Yu.M. Romanovsky, V.P. Trifonenkov “Energetics and stochastic dynamics of intraneuron transport” Phys. Usp. 59 121–140 (2016)
M.A. Tsyganov, V.N. Biktashev et al “Waves in systems with cross-diffusion as a new class of nonlinear waves” Phys. Usp. 50 263–286 (2007)
G.N. Borisyuk, R.M. Borisyuk et al “Models of neural dynamics in brain information processing — the developments of ’the decade’” Phys. Usp. 45 1073–1095 (2002)
H.D. Abarbanel, M.I. Rabinovich et al “Synchronisation in neural networks” Phys. Usp. 39 337–362 (1996)
L.V. Doronina-Amitonova, I.V. Fedotov et al “Neurophotonics: optical methods to study and control the brain” Phys. Usp. 58 345–364 (2015)
M.I. Rabinovich, M.M. Sushchik “The regular and chaotic dynamics of structures in fluid flows” Sov. Phys. Usp. 33 (1) 1–35 (1990)
M.I. Rabinovich “Stochastic self-oscillations and turbulence” Sov. Phys. Usp. 21 443–469 (1978)
D.K. Belashchenko “Computer simulation of liquid metals” Phys. Usp. 56 1176–1216 (2013)
Yu.M. Romanovsky, A.N. Tikhonov “Molecular energy transducers of the living cell. Proton ATP synthase: a rotating molecular motor” Phys. Usp. 53 893–914 (2010)
L.G. Titarchuk, E.V. Mikheeva, V.N. Lukash “Generation of X-ray radiation in the inner regions of accretion disks around black holes, neutron stars, and white dwarfs” Phys. Usp. 66 885–913 (2023)
S.V. Popruzhenko, A.M. Fedotov “Dynamics and radiation of charged particles in ultra-intense laser fields” Phys. Usp. 66 460–493 (2023)
A.V. Tutukov, A.M. Cherepashchuk “Evolution of close binary stars: theory and observations” Phys. Usp. 63 209–244 (2020)

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