Reservoir computing using self-sustained oscillations in a locally connected neural network

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Sept. 19, 2023

Understanding how the structural organization of neural networks influences their computational capabilities is great interest to both machine learning and neuroscience communities. In our previous work, we introduced a novel system, called reservoir basal dynamics (reBASICS), which features modular architecture (small-sized random networks) capable reducing chaoticity activity producing stable self-sustained limit cycle activities. The integration these cycles achieved by linear summation weights, arbitrary time series are learned modulating weights. Despite its excellent performance, interpreting structure isolated small as brain network has posed significant challenge. Here, investigate local connectivity, well-known characteristic networks, contributes system generates based on empirical experiments. Moreover, present performance locally connected reBASICS in two tasks: motor timing task Lorenz series. Although was inferior that reBASICS, could learn tens seconds while constant units ten milliseconds. This work indicates locality connectivity may contribute generation oscillations long-term series, well economy wiring cost.

Language: Английский

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A biomarker discovery framework for childhood anxiety

Frontiers in Psychiatry, Journal Year: 2023, Volume and Issue: 14

Published: July 17, 2023

Anxiety is the most common manifestation of psychopathology in youth, negatively affecting academic, social, and adaptive functioning increasing risk for mental health problems into adulthood. disorders are diagnosed only after clinical symptoms emerge, potentially missing opportunities to intervene during critical early prodromal periods. In this study, we used a new empirical approach extracting nonlinear features electroencephalogram (EEG), with goal discovering differences brain electrodynamics that distinguish children anxiety from healthy children. Additionally, examined whether could externalizing anxiety. We novel supervised tensor factorization method extract latent factors repeated multifrequency EEG measures longitudinal sample assessed infancy at ages 3, 5, 7 years age. first validity by showing calendar age highly correlated complexity (r = 0.77). then computed separately distinguishing controls using 5-fold cross validation scheme similarly controls. found derived recordings were required an disorder controls; infancy, 3 years, or 5 alone insufficient. However, two (5, years) three (3, gave much better results than year alone. Externalizing be detected 3- data, also giving any single snapshot. Further, sex assigned birth was important covariate improved accuracy both groups, birthweight as modestly disorders. Recordings infant did not contribute classification either This study suggests extracted childhood promising candidate biomarkers if chosen appropriate ages.

Language: Английский

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Physical Reservoir Computing Enabled by Solitary Waves and Biologically-Inspired Nonlinear Transformation of Input Data

Published: Jan. 4, 2024

Reservoir computing (RC) systems can efficiently forecast chaotic time series using nonlinear dynamical properties of an artificial neural network random connections. The versatility RC has motivated further research on both hardware counterparts traditional algorithms and more efficient RC-like schemes. Inspired by the processes in a living biological brain solitary waves excited surface flowing liquid film, this paper we experimentally validate physical system that substitutes effect randomness for transformation input data. Carrying out all operations microcontroller with minimal computational power, demonstrate so-designed serves as technically simple counterpart to `next-generation’ improvement algorithm.

Language: Английский

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Biologically plausible models of cognitive flexibility: merging recurrent neural networks with full-brain dynamics

Current Opinion in Behavioral Sciences, Journal Year: 2024, Volume and Issue: 56, P. 101351 - 101351

Published: Feb. 6, 2024

Cognitive flexibility, a cornerstone of human cognition, enables us to adapt shifting environmental demands. This brain function has been widely explored using computational modeling, although oftentimes these models focus on the operational dimension cognitive flexibility and do not retain sufficient level neurobiological detail lead electrophysiological or neuroimaging insights. In this review, we explore recent advances future directions neurobiologically plausible flexibility. We first cover progress in recurrent neural network trained perform flexible tasks, followed by discussion how whole-brain large-scale have approached distributed nature functions. Ultimately, propose here hybrid framework which both modeling philosophies converge, advocating for balanced approach that merges power with realistic spatiotemporal dynamics activity, early examples direction.

Language: Английский

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Sequence Prediction and Classification of Echo State Networks

Mathematics, Journal Year: 2023, Volume and Issue: 11(22), P. 4640 - 4640

Published: Nov. 14, 2023

The echo state network is a unique form of recurrent neural network. Due to its feedback mechanism, it exhibits superior nonlinear behavior compared traditional networks and highly regarded for simplicity efficiency in computation. In recent years, as development has progressed, the security threats faced by have increased. To detect counter these threats, analysis traffic become crucial research focus. demonstrated exceptional performance sequence prediction. this article, we delve into impact on time series. We enhanced model increasing number layers adopting different data input approach. apply predict chaotic systems that appear ostensibly regular but are inherently irregular. Additionally, utilize classification sound data. Upon evaluating using root mean squared error micro-F1, observed our commendable accuracy stability.

Language: Английский

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