Neural recording technologies now enable simultaneous recording of population activity across many brain regions, motivating the development of data-driven models of inter-regional communication. However, existing models can struggle to disentangle the influences that drive recorded population activity, leading to inaccurate portraits of 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同步记录多个脑区的群体活动，这推动了跨脑区通信数据驱动模型的发展。然而，现有模型难以有效解耦驱动记录群体活动的多重影响，导致对通信模式的刻画存在偏差。本文提出基于动力系统的多区域潜变量因子分析模型（Multi-Region Latent Factor Analysis via Dynamical Systems，MR-LFADS），该序列变分自编码器旨在解耦跨脑区通信、未观测区域的输入以及局部神经群体动力学。我们通过数十个任务训练的多区域网络仿真实验证明，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能预测模型拟合过程中未涉及的全脑电路扰动效应。这些基于合成与真实神经数据的验证表明，MR-LFADS有望成为揭示全脑信息处理原理的重要工具。