Boston University.
Massachusetts Institute of Technology.
J Cogn Neurosci. 2020 Aug;32(8):1455-1465. doi: 10.1162/jocn_a_01569. Epub 2020 May 7.
Large-scale neuronal recording techniques have enabled discoveries of population-level mechanisms for neural computation. However, it is not clear how these mechanisms form by trial-and-error learning. In this article, we present an initial effort to characterize the population activity in monkey prefrontal cortex (PFC) and hippocampus (HPC) during the learning phase of a paired-associate task. To analyze the population data, we introduce the normalized distance, a dimensionless metric that describes the encoding of cognitive variables from the geometrical relationship among neural trajectories in state space. It is found that PFC exhibits a more sustained encoding of the visual stimuli, whereas HPC only transiently encodes the identity of the associate stimuli. Surprisingly, after learning, the neural activity is not reorganized to reflect the task structure, raising the possibility that learning is accompanied by some "silent" mechanism that does not explicitly change the neural representations. We did find partial evidence on the learning-dependent changes for some of the task variables. This study shows the feasibility of using normalized distance as a metric to characterize and compare population-level encoding of task variables and suggests further directions to explore learning-dependent changes in the neural circuits.
大规模神经元记录技术使人们能够发现神经计算的群体机制。然而，目前尚不清楚这些机制是如何通过试错学习形成的。在本文中，我们首次尝试描述猴子前额叶皮层（PFC）和海马体（HPC）在配对联想任务学习阶段的群体活动。为了分析群体数据，我们引入了归一化距离，这是一种无量纲的度量标准，用于描述状态空间中神经轨迹之间的几何关系从认知变量的编码。结果发现，PFC 对视觉刺激的编码更为持续，而 HPC 仅对联想刺激的身份进行短暂编码。令人惊讶的是，学习后，神经活动并没有重新组织以反映任务结构，这表明学习伴随着一些不明确改变神经表示的“沉默”机制。我们确实发现了一些关于任务变量的学习依赖性变化的部分证据。这项研究表明，使用归一化距离作为度量标准来描述和比较任务变量的群体水平编码是可行的，并为探索神经回路中的学习依赖性变化提供了进一步的方向。
