Decoding Dynamic Visual Experience from Calcium Imaging via Cell-Pattern-Aware Pretraining

Sangyoon Bae; Mehdi Azabou; Blake Richards; Jiook Cha

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arXiv · 2510.18516

Decoding Dynamic Visual Experience from Calcium Imaging via Cell-Pattern-Aware Pretraining

Sangyoon Bae·Mehdi Azabou·Blake Richards·Jiook Cha

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Abstract

Neural recordings exhibit a distinctive form of heterogeneity rooted in differences in cell types, intrinsic circuit dynamics, and stochastic stimulus-response variability that goes beyond ordinary dataset variability, mixing statistically regular neurons with highly stochastic, stimulus-contingent ones within the same dataset. This heterogeneity poses a challenge for self-supervised learning (SSL) -- learnable statistical regularity -- thereby destabilizing representation learning and limiting reliable scaling. We introduce POYO-CAP (Cell-pattern Aware Pretraining), a biologically grounded hybrid pretraining strategy that first trains with masked reconstruction plus lightweight auxiliary supervision on statistically regular neurons -- identified via skewness and kurtosis -- and then fine-tunes on more stochastic populations. On the Allen Brain Observatory dataset, this curriculum yields 12--13\% relative improvements over from-scratch training and enables smooth, monotonic scaling with model size, whereas baselines trained on mixed populations plateau or destabilize. By making statistical predictability an explicit data-selection criterion, POYO-CAP turns neural heterogeneity into a scalable learning advantage for robust neural decoding.

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Sangyoon Bae, Mehdi Azabou, Blake Richards, Jiook Cha. 2026-05-08. Decoding Dynamic Visual Experience from Calcium Imaging via Cell-Pattern-Aware Pretraining. https://arxiv.org/abs/2510.18516

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