Editorial: Bridging computation, biophysics, medicine, and engineering in neural circuits

hardware, bioengineering, and medicine. A unifying theme across these works is the investigation of 32 how structured dynamics and learning rules enable robust information processing, cognition, and 33 recovery from damage in biological neural systems, as well as how these principles can guide the 34 development of computational and neuroengineering models. 35Several studies in this Research Topic focus on recurrent network dynamics as a foundation for 36 cortical information processing. Yonemura and Katori extended reservoir computing-based cortical 37 models to account for multi-sensory integration. By combining predictive coding with reliability-38 weighted error feedback, their model allows a recurrent reservoir to extract temporal context while 39 dynamically adjusting the influence of each sensory modality depending on its noise level.

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Publication Details

Journal
Frontiers in Neural Circuits
Published
2026-01-21
DOI
https://doi.org/10.3389/fncir.2026.1776224
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
0.00

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Editorial: Bridging computation, biophysics, medicine, and engineering in neural circuits

Haruyuki Kamiya, Hideaki Yamamoto, Toshiaki Omori, Shigeo Sato et al.
Frontiers in Neural Circuits
Neural Networks and Reservoir Computing
article

Editorial: Bridging computation, biophysics, medicine, and engineering in neural circuits

Haruyuki Kamiya, Hideaki Yamamoto, Toshiaki Omori, Shigeo Sato, Juan José Soriano
article en

Abstract

hardware, bioengineering, and medicine. A unifying theme across these works is the investigation of 32 how structured dynamics and learning rules enable robust information processing, cognition, and 33 recovery from damage in biological neural systems, as well as how these principles can guide the 34 development of computational and neuroengineering models. 35Several studies in this Research Topic focus on recurrent network dynamics as a foundation for 36 cortical information processing. Yonemura and Katori extended reservoir computing-based cortical 37 models to account for multi-sensory integration. By combining predictive coding with reliability-38 weighted error feedback, their model allows a recurrent reservoir to extract temporal context while 39 dynamically adjusting the influence of each sensory modality depending on its noise level.

Frontiers in Neural CircuitsVol. 20
Tohoku University (JP), Hokkaido University (JP), Institute of Neurobiology (BG), Kobe University (JP), Universitat de Barcelona (ES)
Ministerio de Ciencia, Innovación y Universidades, European Commission, Generalitat de Catalunya, Japan Society for the Promotion of Science, Japan Science and Technology Agency, Research Institute of Electrical Communication, Tohoku University, European Regional Development Fund, Agencia Estatal de Investigación, Core Research for Evolutional Science and Technology
Quality Education
Openalex Percentile: Top 83%
Neural Networks and Reservoir Computing
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