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

TL;DR

This paper introduces POYO-CAP, a biologically inspired pretraining method that leverages neural heterogeneity to improve decoding of calcium imaging data, demonstrating significant performance gains and scalable learning.

Contribution

The paper presents a novel cell-pattern-aware pretraining strategy that explicitly accounts for neural heterogeneity, enhancing stability and scalability in neural decoding tasks.

Findings

POYO-CAP achieves 12-13% relative improvement over from-scratch training.

It enables smooth, monotonic scaling with model size.

Baseline methods plateau or destabilize on mixed neural populations.

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.