Learning Behaviorally Relevant Dynamics in Multimodal Neural Recordings

原文：Learning Behaviorally Relevant Dynamics in Multimodal Neural Recordings

作者：Parastoo Azizeddin, Eray Erturk, Maryam M. Shanechi

来源：arXiv cs.AI（人工智能）

正文

Computer Science > Artificial Intelligence

arXiv:2611.05167v1 (cs)

[Submitted on 3 Nov 2026]

Title:Learning Behaviorally Relevant Dynamics in Multimodal Neural Recordings

Authors:Parastoo Azizeddin, Eray Erturk, Maryam M. Shanechi

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Abstract:Multimodal neural data can enable a more complete understanding of brain dynamics underlying behavior. Modalities such as spiking activity, local field potentials (LFPs), and behavior capture diverse spatiotemporal aspects of brain processes, yet prior neural-behavioral models that separate sources of variability rely on a single neural modality. By leveraging these complementary strengths, multimodal neural fusion can provide a unified, rich representation of brain-behavior processes and address the limitations of single-modality analyses. Here we develop BREM-NET, a nonlinear multimodal dynamical model that integrates behavior with multiple neural modalities having distinct statistical characteristics and temporal resolutions. BREM-NET performs multimodal neural fusion during inference while separating behaviorally relevant and residual neural dynamics via a structured learning framework. In a public multimodal non-human primate dataset with simultaneous spiking and local field potential recordings during a 2D reaching task, BREM-NET separates these two types of dynamics and learns them more accurately, as reflected in better behavior decoding, better neural prediction, and stronger latent alignment with behavior, and it retains these capabilities when neural modalities are asynchronous or suffer dropouts, which are major challenges in real-world recordings. This framework provides a new tool for studying behaviorally relevant neural computations across spatiotemporal scales of brain activity.

Subjects:

Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)

Cite as:

arXiv:2611.05167 [cs.AI]

(or

arXiv:2611.05167v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.05167

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arXiv-issued DOI via DataCite (pending registration)

主题

人工智能 · 机器学习 · 神经计算

由「前沿雷达」于 2026-11-04 采集。正文取自原文页面，已保留出处链接。标题与正文版权归原作者所有。