Brain-inspired reservoir computing framework for complex time-series prediction.

Authors

Pei Ma, Xubin Wang, Hanshuo Qiu, Jizhao Liu, Yide Ma, Ludovico Minati

Published in

iScience. Volume 29. Issue 8. Pages 116719. Aug 21, 2026. Epub Jul 23, 2026.

Abstract

Time-series prediction based on historical data is essential in numerous scientific fields, such as weather prediction and financial markets analysis. However, obtaining strong predictive accuracy together with high computational efficiency remains challenging. To address this challenge, we propose a brain-inspired neural network-based echo state network (BINN-ESN). It uses a modified continuous coupled neural network (MCCNN) as the neural model, which is inspired by the mammalian visual cortex. Our results indicate a system-dependent trade-off: While deep learning baselines generally achieve stronger single-step accuracy, BINN-ESN shows stronger long-term predictive stability on most evaluated systems and requires substantially less total computation than the GPU-accelerated long short-term memory (LSTM) baseline in long-term experiments. Our code is available at https://github.com/Jizhao-Liu/code-for-BINN-ESN.

PMID:
42542677
Bibliographic data and abstract were imported from PubMed on 02 Aug 2026.

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