Self-Supervised Foundation Model for Calcium-imaging Population Dynamics

Xinhong Xu, Yimeng Zhang, Qichen Qian, Yuanlong Zhang

Abstract:

Recent work suggests that large-scale, multianimal modeling can significantly improve neural recording analysis. However, for functional
calcium traces, existing approaches remain taskspecific, limiting transfer across common neuroscience objectives. To address this challenge,
we propose CalM, a self-supervised neural foundation model trained solely on neuronal calcium
traces and adaptable to multiple downstream tasks,
including forecasting and decoding. Our key contribution is a pretraining framework, composed
of a high-performance tokenizer mapping singleneuron traces into a shared discrete vocabulary,
and a dual-axis autoregressive transformer modeling dependencies along both the neural and the
temporal axis. We evaluate CalM on a largescale, multi-animal, multi-session dataset. On
the neural population dynamics forecasting task,
CalM outperforms strong specialized baselines
after pretraining. With a task-specific head, CalM
further adapts to the behavior decoding task and
achieves superior results compared with supervised decoding models. Moreover, linear analyses of CalM representations reveal interpretable
functional structures beyond predictive accuracy.
Taken together, we propose a novel and effective
self-supervised pretraining paradigm for foundation models based on calcium traces, paving the
way for scalable pretraining and broad applications in functional neural analysis.