Work

Real-time classification of cortical slow-wave states by a machine learning model

Year: 2026

Type: article

Abstract: During slow-wave sleep, cortical slow oscillations—composed of alternating Up/Down states (UDS)—are crucial for memory consolidation. We classified UDS from local field potentials using machine learning models, specifically a hybrid CNN + RNN and a Transformer architecture, trained on labels derived from membrane potential recordings. Both models outperformed conventional methods, achieving superior classification accuracy and markedly reducing classification errors. Notably, the Transformer model also enabled real-time inference while maintaining robust performance. This real-time classification capability facilitates closed-loop feedback experiments to manipulate neuronal activity contingent on UDS phase, providing a powerful tool for investigating the causal mechanisms underlying sleep-dependent memory consolidation. • We established machine learning-based methods for Up/Down classification • The combined CNN + RNN model achieves robustness for noise • The transformer model achieves real-time Up/Down classification

Source: Neuroscience Research

DOI: 10.1016/j.neures.2026.105024

Authors: Minato Uemura, Hiroyuki Mizuno, Yuji Ikegaya, Tetsuhiko Kashima

Institutions: The University of Tokyo, National Institute of Information and Communications Technology

Language: English

Topic: Neural dynamics and brain function

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,770

Funders: Japan Agency for Medical Research and Development, University of Tokyo, Japan Society for the Promotion of Science, Exploratory Research for Advanced Technology