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.

翻译：神经记录技术现已能够同时记录多个脑区的群体活动，这推动了数据驱动的脑区间通信模型的发展。然而，现有模型在分离影响记录神经群体的信号源方面存在困难，导致对区域间通信的描述不够准确。本文提出多脑区动态系统潜因子分析（MR-LFADS），这是一种顺序变分自编码器，旨在分离区域间通信、来自未观测区域的输入以及局部神经群体动态。我们证明，在数十个任务训练多区域网络的模拟中，MR-LFADS在识别通信方面优于现有方法。当应用于大规模电生理数据时，MR-LFADS能够预测在模型拟合过程中被保留的环路扰动对全脑的影响。这些在合成和真实神经数据上的验证表明，MR-LFADS是发现全脑信息处理原理的有力工具。