Neural circuits are composed of multiple regions, each with rich dynamics and engaging in communication with other regions. The combination of local, within-region dynamics and global, network-level dynamics is thought to provide computational flexibility. However, the nature of such multiregion dynamics and the underlying synaptic connectivity patterns remain poorly understood. Here, we study the dynamics of recurrent neural networks with multiple interconnected regions. Within each region, neurons have a combination of random and structured recurrent connections. Motivated by experimental evidence of communication subspaces between cortical areas, these networks have low-rank connectivity between regions, enabling selective routing of activity. These networks exhibit two interacting forms of dynamics: high-dimensional fluctuations within regions and low-dimensional signal transmission between regions. To characterize this interaction, we develop a dynamical mean-field theory to analyze such networks in the limit where each region contains infinitely many neurons, with cross-region currents as key order parameters. Regions can act as both generators and transmitters of activity, roles that we show are in conflict. Specifically, taming the complexity of activity within a region is necessary for it to route signals to and from other regions. Unlike previous models of routing in neural circuits, which suppressed the activities of neuronal groups to control signal flow, routing in our model is achieved by exciting different high-dimensional activity patterns through a combination of connectivity structure and nonlinear recurrent dynamics. This theory provides insight into the interpretation of both multiregion neural data and trained neural networks.

翻译：神经回路由多个脑区构成，每个脑区都具有丰富的动力学特性，并与其他脑区进行通信。局部（脑区内）动力学与全局（网络层级）动力学的结合被认为提供了计算灵活性。然而，此类多区域动力学的本质及其潜在的突触连接模式仍未被充分理解。本研究探讨了具有互连区域结构的循环神经网络动力学特性。在每个区域内，神经元同时具有随机和结构化循环连接。受皮层间通信子空间实验证据的启发，这些网络在区域间采用低秩连接结构，能够实现活动的选择性路由。网络表现出两种相互作用的动力学形式：区域内部的高维波动和区域间的低维信号传输。为表征这种相互作用，我们发展了动力学平均场理论，在每区域包含无穷多个神经元的极限下分析此类网络，并以跨区域电流作为关键序参量。脑区既是活动的产生者也是传递者，我们证明这两种角色存在冲突：具体而言，抑制区域内部活动的复杂性是其向其他区域路由信号的先决条件。与以往通过抑制神经元群体活动来控制信号流的神经回路路由模型不同，本模型通过连接结构和非线性循环动力学的组合，激发不同高维活动模式实现路由。该理论为理解多区域神经数据及训练后的神经网络提供了新视角。