瑞士苏黎世大学和苏黎世联邦理工学院Valerio Mante和Aniruddh R. Galgali团队的一项最新研究发现，残差动力学可用于解决神经计算中存在的重复计算。该项研究成果发表在2023年1月12日出版的《自然-神经科学》上。

研究表明，神经回路循环动力学推断中的部分难题可通过对神经残差动力学的细粒度分析来解决，即在给定任务情况下分析平均神经群体轨迹周围的逐个试验变异性。在基于扫描的感知决策任务中，猕猴前额叶皮层（PFC）的残差动力学揭示了与时间相关但始终稳定的循环动力学，并表明扫描期间PFC轨迹中明显的旋转结构是由上游区域的输入诱导的。残差动力学的特性限制了PFC对决策和扫描生成的可能贡献，并提出了一条通过大规模神经记录和有针对性的因果扰动来全面表征分布式神经计算的途径。

研究人员表示，将神经活动与行为联系起来需要了解神经计算如何从分布、反复连接的神经群体中协调产生。然而，从神经回路的部分记录中推断循环动力学的性质存在相当大的挑战。

附：英文原文

Title: Residual dynamics resolves recurrent contributions to neural computation

Author: Galgali, Aniruddh R., Sahani, Maneesh, Mante, Valerio

Issue&Volume: 2023-01-12

Abstract: Relating neural activity to behavior requires an understanding of how neural computations arise from the coordinated dynamics of distributed, recurrently connected neural populations. However, inferring the nature of recurrent dynamics from partial recordings of a neural circuit presents considerable challenges. Here we show that some of these challenges can be overcome by a fine-grained analysis of the dynamics of neural residuals—that is, trial-by-trial variability around the mean neural population trajectory for a given task condition. Residual dynamics in macaque prefrontal cortex (PFC) in a saccade-based perceptual decision-making task reveals recurrent dynamics that is time dependent, but consistently stable, and suggests that pronounced rotational structure in PFC trajectories during saccades is driven by inputs from upstream areas. The properties of residual dynamics restrict the possible contributions of PFC to decision-making and saccade generation and suggest a path toward fully characterizing distributed neural computations with large-scale neural recordings and targeted causal perturbations.

DOI: 10.1038/s41593-022-01230-2

Source: https://www.nature.com/articles/s41593-022-01230-2