哥伦比亚大学Stefano Fusi小组的一项最新研究揭示皮质层次结构中很少有分类的、高度可分离的表示。相关论文发表在2026年7月15日出版的《自然》杂志上。

在这里，课题组系统地分析了皮层神经元如何在一个复杂的任务中编码43个皮层区域的认知、感觉和运动变量（来自国际脑实验室公共Brainwide Map数据集的14000多个单位），并研究了这些属性如何在感觉-认知皮层层次中变化。研究小组发现神经编码的结构是尺度相关的。在整个皮层尺度上，神经选择性是分类的，并以一种反映其解剖连通性的方式组织在各个区域。

然而，在单个区域内，分类表征是罕见的，并且仅限于初级感觉区域，而神经元的反应是非常多样化的。通过理论论证和经验证据，该研究团队证明了神经反应的多样性能够实现高维表征，因此具有高可分离性，允许线性读数以许多任意方式分离实验条件。事实上，当考虑到每个区域实际编码的信息时，所有皮质区域都表现出最大的可分离性。他们的研究结果表明，皮层回路优先考虑多样性而不是分类结构，支持一种面向高维、高度可分离的神经表征的计算机制。

据悉，神经科学中一个长期存在的争论是，单个神经元是否被组织成功能不同的群体，以不同的方式编码信息（分类表征），以及对神经计算的影响。

附：英文原文

Title: Rarely categorical, highly separable representations along the cortical hierarchy

Author: Posani, Lorenzo, Wang, Shuqi, Muscinelli, Samuel P., Paninski, Liam, Fusi, Stefano

Issue&Volume: 2026-07-15

Abstract: A long-standing debate in neuroscience concerns whether individual neurons are organized into functionally distinct populations that encode information differently (categorical representations1,2,3) and the implications for neural computation. Here we systematically analysed how cortical neurons encode cognitive, sensory and movement variables across 43 cortical regions during a complex task (14,000+ units from the International Brain Laboratory public Brainwide Map dataset4) and studied how these properties change across the sensory–cognitive cortical hierarchy5. We found that the structure of the neural code was scale dependent. At the whole-cortex scale, neural selectivity was categorical and organized across regions in a way that reflected their anatomical connectivity. However, within individual regions, categorical representations were rare and limited to primary sensory areas, and neuronal responses were instead very diverse. With theoretical arguments and empirical evidence, we demonstrate that the diversity of neural responses enables high-dimensional representations and therefore high separability, allowing linear readouts to separate experimental conditions in many arbitrary ways. Indeed, when accounting for information that is actually encoded in each area, all cortical regions exhibit maximal separability. Our results indicate that cortical circuits prioritize diversity over categorical structure, supporting a computational regime geared towards high-dimensional, highly separable neural representations.

DOI: 10.1038/s41586-026-10668-4

Source: https://www.nature.com/articles/s41586-026-10668-4