论文标题
量化从大型大脑网络分布式工作记忆的吸引子景观和过渡路径
Quantifying the attractor landscape and transition path of distributed working memory from large-scale brain network
论文作者
论文摘要
许多认知过程，包括工作记忆，募集多个分布式交互的大脑区域来编码信息。如何理解工作记忆的潜在认知功能机制是一个具有挑战性的问题，涉及来自多个大脑区域以及大脑状态之间的随机过渡动态的神经回路构型。能量景观的想法提供了一种研究分布式认知功能系统中全球稳定性和随机过渡动力学的工具。但是，如何量化现实的大型大脑网络中的能量格局尚不清楚。在这里，基于大规模猕猴皮层的解剖学约束计算模型，我们量化了分布式工作记忆的基本可固定吸引子景观。在没有外部刺激的情况下，景观表现出三个稳定的吸引子，一个自发状态和两个记忆状态。在“吸引者景观框架”中，工作记忆功能受景观地形的变化和根据任务要求的转换的控制。从景观地形中推断出的屏障高度量化了记忆状态的全球稳定性和鲁棒性对非选择性随机波动和干扰物刺激的稳定性。通过最小动作路径方法识别的动力学过渡路径表明，在两个内存状态之间的切换期间，自发状态作为中间状态，存储在具有较高层次结构的皮质区域中的内存更稳定，信息流遵循层次结构的方向。这些结果为分布式工作记忆函数的潜在机制提供了新的见解，并且可以将景观和动力学路径方法应用于大脑网络中的其他与认知功能有关的问题。
Many cognitive processes, including working memory, recruit multiple distributed interacting brain regions to encode information. How to understand the underlying cognition function mechanism of working memory is a challenging problem, which involves neural circuit configuration from multiple brain regions as well as stochastic transition dynamics between brain states. The energy landscape idea provides a tool to study the global stability and stochastic transition dynamics in the distributed cognitive function system. However, how to quantify the energy landscape in a realistic large-scale brain network remains unclear. Here, based on an anatomically constrained computational model of large-scale macaque cortex, we quantified the underlying multistable attractor landscape of distributed working memory. In the absence of external stimulation, the landscape exhibits three stable attractors, a spontaneous state, and two memory states. In the attractor landscape framework, the working memory function is governed by the change of landscape topography and the switch of system state according to the task requirement. The barrier height inferred from landscape topography quantifies the global stability of memory state and robustness to non-selective random fluctuations and distractor stimuli. The kinetic transition path identified by the minimum action path approach reveals that the spontaneous state serves as an intermediate state during the switch between the two memory states, the memory stored in the cortical area with higher hierarchy is more stable, and information flow follows the direction of hierarchical structure. These results provide new insights into the underlying mechanism of distributed working memory function, and the landscape and kinetic path approach can be applied to other cognitive function-related problems in brain networks.
