发表机构
ShanechiLab（沙内奇实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对宽场钙成像建模受限问题，提出多主体模型WiCAT，利用自监督预训练，引入图谱对齐的时空令牌化方案，能跨主体、任务和数据集迁移，实现零样本行为解码及遗漏脑区重建，优于基线模型。
AI 中文摘要
大规模多主体宽场钙成像为全脑皮层动力学研究提供了前所未有的途径。然而，宽场记录中的高维度、复杂时空结构以及大量与任务无关的活动，很大程度上限制了建模工作仅用于单会话分析，限制了可扩展性和泛化性。虽然已经探索了针对某些神经模态的多主体预训练模型，但宽场钙成像的多主体模型尚未得到验证；此外，跨神经模态的多主体模型的主体不变零样本行为解码仍然难以实现。作为宽场数据基础建模的第一步，我们引入了WiCAT，这是一个多主体模型，利用自监督预训练不仅优于单会话模型，还能对未见主体进行零样本行为解码。WiCAT引入了一种无特定会话组件的基于图谱的令牌化方案，并学习全局共享的时空表示。在多个宽场数据集上，预训练模型支持轻量级下游解码，可跨主体、任务和数据集进行迁移，且优于基线模型。值得注意的是，该模型还在未见主体上实现了强大的零样本连续行为解码和遗漏脑区重建。
英文摘要
Large-scale, multi-subject widefield calcium imaging provides unprecedented access to brain-wide cortical dynamics. However, the high dimensionality, complex spatiotemporal structure, and substantial task-irrelevant activity in widefield recordings have largely restricted modeling efforts to single-session analyses, limiting scalability and generalization. While multi-subject pretrained models have been explored for some neural modalities, multi-subject models for widefield calcium imaging have not yet been demonstrated; further, subject-invariant zero-shot behavior decoding remains elusive for multi-subject models across neural modalities more broadly. As a first step toward foundation modeling of widefield data, we introduce WiCAT, a multi-subject model that leverages self-supervised pretraining to both outperform single-session models and enable zero-shot behavior decoding on unseen subjects. WiCAT introduces an atlas-grounded tokenization scheme without session-specific components and learns globally shared spatiotemporal representations. Across multiple widefield datasets, the pretrained model supports lightweight downstream decoding, transfers across subjects, tasks, and datasets, and outperforms baseline models. Notably, the model also achieves robust zero-shot continuous behavior decoding and left-out brain region reconstruction on unseen subjects.
CommentsPublished at the 43rd International Conference on Machine Learning (ICML) 2026. Code available at: https://github.com/ShanechiLab/WiCAT
Journal refICML 2026
