Liu Belle, Sacks Jacob, Golub Matthew D
Graduate Program in Neuroscience, University of Washington.
Paul G. Allen School of Computer Science & Engineering, University of Washington.
ArXiv. 2025 Aug 18:arXiv:2506.19094v2.
Neural recording technologies now enable simultaneous recording of population activity across many brain regions, motivating the development of data-driven models of communication between brain regions. However, existing models can struggle to disentangle the sources that influence recorded neural populations, leading to inaccurate portraits of inter-regional communication. Here, we introduce Multi-Region Latent Factor Analysis via Dynamical Systems (MR-LFADS), a sequential variational autoencoder designed to disentangle inter-regional communication, inputs from unobserved regions, and local neural population dynamics. We show that MR-LFADS outperforms existing approaches at identifying communication across dozens of simulations of task-trained multi-region networks. When applied to large-scale electrophysiology, MR-LFADS predicts brain-wide effects of circuit perturbations that were held out during model fitting. These validations on synthetic and real neural data position MR-LFADS as a promising tool for discovering principles of brain-wide information processing.
神经记录技术现在能够同时记录多个脑区的群体活动，这推动了脑区之间通信的数据驱动模型的发展。然而，现有模型难以区分影响记录的神经群体的来源，导致对区域间通信的描绘不准确。在这里，我们介绍了通过动态系统进行的多区域潜在因子分析（MR-LFADS），这是一种顺序变分自编码器，旨在区分区域间通信、来自未观察区域的输入以及局部神经群体动态。我们表明，在识别经过任务训练的多区域网络的数十次模拟中的通信时，MR-LFADS优于现有方法。当应用于大规模电生理学时，MR-LFADS预测了在模型拟合期间被排除的电路扰动对全脑的影响。这些对合成和真实神经数据的验证表明，MR-LFADS是发现全脑信息处理原理的一个有前途的工具。
