NeuroLens: Learning Latent Embeddings of Neural Semantics from Chronic Recordings

Hanrui Lyu; Baiyuan Chen; Tianshu Tan; Matthew R. Whiteway; Maxwell D. Melin; Ji Xia; Linyang He; Bradly C. Stadie; Anne Churchland; Liam Paninski; Yizi Zhang

arXiv · 2610.02864

NeuroLens: Learning Latent Embeddings of Neural Semantics from Chronic Recordings

Hanrui Lyu·Baiyuan Chen·Tianshu Tan·Matthew R. Whiteway·Maxwell D. Melin·Ji Xia·Linyang He·Bradly C. Stadie·Anne Churchland·Liam Paninski·Yizi Zhang

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Abstract

Understanding how neural activity represents higher-order cognition and how these representations evolve over time has long been a central pursuit in neuroscience. However, current analytical tools cannot easily distinguish representational plasticity from recording instability in chronic neural recordings. Here, we introduce NeuroLens (Latent Embeddings of Neural Semantics), a self-supervised model based on the Joint-Embedding Predictive Architecture (JEPA) framework that learns denoised, semantically informative latents from chronic neural recordings. An adaptive encoder maps changing neural populations into a common latent space, while a temporal predictor learns structure that supports prediction of future latent states. By predicting in latent space, NeuroLens captures temporally predictable structure and reduces sensitivity to transient, recording-specific variability. Across chronic intracortical data in mice and humans, the learned representations improve decoding of decision-making and semantic task variables. Multi-day pretraining enables generalization to future sessions, rapid few-shot adaptation to unseen neural populations, and more stable decoding over time than state-of-the-art baselines. Together, these results establish NeuroLens as a new paradigm for studying how neural representations change during learning and over long timescales.

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Hanrui Lyu, Baiyuan Chen, Tianshu Tan, Matthew R. Whiteway, Maxwell D. Melin, Ji Xia, Linyang He, Bradly C. Stadie, Anne Churchland, Liam Paninski, Yizi Zhang. 2026-10-02. NeuroLens: Learning Latent Embeddings of Neural Semantics from Chronic Recordings. https://arxiv.org/abs/2610.02864

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