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

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 finetunes 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. However, successful SSL relies on statistical regularities in data, as evidenced by masked modeling and sequence prediction in structured domains such as language (