English(EN) BrainVista: Modeling Naturalistic Brain Dynamics as Multimodal Next-Token Prediction

BrainVista框架使用多模态下一词元预测来模拟大脑动力学

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

研究人员开发了BrainVista，一个新颖的多模态自回归框架，旨在利用自然fMRI数据预测未来大脑状态。该框架通过整合历史大脑活动与连续的感官输入来应对预测大脑动力学的挑战，同时仔细管理迟缓的血流动力学响应与快速变化的刺激之间的时间对齐。BrainVista采用网络化词元化器和空间混合器头部等专用组件来优化跨网络交互，并使用刺激到大脑（S2B）掩码来维持因果条件而不发生未来状态泄露。在三个大规模fMRI基准上的评估表明，BrainVista在长期条件自回归预测方面优于现有方法。 AI

影响这项研究推动了下一词元预测模型在复杂生物系统中的应用，可能影响未来在神经科学和认知建模中的AI应用。

排序理由该集群包含一篇详细介绍大脑动力学新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Xuanhua Yin, Runkai Zhao, Lina Yao, Weidong Cai · 2026-11-11 05:00

BrainVista：将自然脑动力学建模为多模态下一词元预测

arXiv:2602.04512v2 Announce Type: replace-cross Abstract: Naturalistic fMRI offers a time-resolved window into cortical dynamics during continuous multimodal experience, where future brain states are jointly governed by endogenous neural context and exogenous sensory drive. Forec…

本文如何被排名

报道来源 [1]

BrainVista：将自然脑动力学建模为多模态下一词元预测

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