
==== Front
bioRxiv
BIORXIV
bioRxiv
2692-8205
Cold Spring Harbor Laboratory

10.1101/2024.09.14.613047
preprint
1
Article
Exploiting correlations across trials and behavioral sessions to improve neural decoding
Zhang Yizi
Lyu Hanrui
Hurwitz Cole
Wang Shuqi
Findling Charles
Hubert Felix
Pouget Alexandre
International Brain Laboratory
Varol Erdem
Paninski Liam
15 9 2024
2024.09.14.613047https://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
http://biorxiv.org/lookup/doi/10.1101/2024.09.14.613047
nihpp-2024.09.14.613047.pdf
Abstract

Traditional neural decoders model the relationship between neural activity and behavior within individual trials of a single experimental session, neglecting correlations across trials and sessions. However, animals exhibit similar neural activities when performing the same behavioral task, and their behaviors are influenced by past experiences from previous trials. To exploit these informative correlations in large datasets, we introduce two complementary models: a multi-session reduced-rank model that shares similar behaviorally-relevant statistical structure in neural activity across sessions to improve decoding, and a multi-session state-space model that shares similar behavioral statistical structure across trials and sessions. Applied across 433 sessions spanning 270 brain regions in the International Brain Laboratory public mouse Neuropixels dataset, our decoders demonstrate improved decoding accuracy for four distinct behaviors compared to traditional approaches. Unlike existing deep learning approaches, our models are interpretable and efficient, uncovering latent behavioral dynamics that govern animal decision-making, quantifying single-neuron contributions to decoding behaviors, and identifying different activation timescales of neural activity across the brain. Code: https://github.com/yzhang511/neural_decoding .
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