[2510.18516] Decoding Dynamic Visual Experience from Calcium Imaging via Cell-Pattern-Aware Pretraining

Quantitative Biology > Neurons and Cognition arXiv:2510.18516 (q-bio) [Submitted on 21 Oct 2025 (v1), last revised 1 Mar 2026 (this version, v2)] Title:Decoding Dynamic Visual Experience from Calcium Imaging via Cell-Pattern-Aware Pretraining Authors:Sangyoon Bae, Mehdi Azabou, Blake Richards, Jiook Cha View a PDF of the paper titled Decoding Dynamic Visual Experience from Calcium Imaging via Cell-Pattern-Aware Pretraining, by Sangyoon Bae and 3 other authors View PDF HTML (experimental) 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 scalin...