美国霍华德休斯医学院Nelson Spruston研究团队取得一项新突破。他们的研究显示，学习在海马体中产生一个正交化状态机。该项研究成果发表在2025年2月12日出版的《自然》上。

在这里，小组对大规模纵向双光子钙成像进行了主题化，以记录海马CA1区域神经元手性的活动，同时小鼠在虚拟现实中学习如何从两个微妙不同的线性轨道上有效地收集奖励。在整个学习过程中，动物行为和海马神经活动都经历了多个阶段，逐渐揭示出任务表征的改善，反映了行为效率的提高。学习过程涉及在最初相似的海马体神经活动内和跨轨道的渐进去关联，最终导致类似于捕获任务固有结构的状态机的正交化表征。这种去关联过程是由单个神经元获得特定于任务状态的反应（即“状态细胞”）驱动的。

尽管各种标准的人工神经网络不能自然地捕捉到这些动态，但克隆结构的因果图（一种隐马尔可夫模型变体）独特地再现了最终的正交状态和动物的学习轨迹。观察到的细胞和群体动态限制了海马体中认知地图形成的机制，指出隐藏状态推断是一种基本的计算原理，对生物和人工智能都有影响。

据介绍，认知地图通过表现空间、时间和抽象的关系赋予动物灵活的智力，这些关系可以以主题来塑造思想、计划和行为。在海马体中已经观察到认知地图，但它们的算法形式和学习机制仍然不清楚。

附：英文原文

Title: Learning produces an orthogonalized state machine in the hippocampus

Author: Sun, Weinan, Winnubst, Johan, Natrajan, Maanasa, Lai, Chongxi, Kajikawa, Koichiro, Bast, Arco, Michaelos, Michalis, Gattoni, Rachel, Stringer, Carsen, Flickinger, Daniel, Fitzgerald, James E., Spruston, Nelson

Issue&Volume: 2025-02-12

Abstract: Cognitive maps confer animals with flexible intelligence by representing spatial, temporal and abstract relationships that can be used to shape thought, planning and behaviour. Cognitive maps have been observed in the hippocampus1, but their algorithmic form and learning mechanisms remain obscure. Here we used large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different linear tracks in virtual reality. Throughout learning, both animal behaviour and hippocampal neural activity progressed through multiple stages, gradually revealing improved task representation that mirrored improved behavioural efficiency. The learning process involved progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent structure of the task. This decorrelation process was driven by individual neurons acquiring task-state-specific responses (that is, ‘state cells’). Although various standard artificial neural networks did not naturally capture these dynamics, the clone-structured causal graph, a hidden Markov model variant, uniquely reproduced both the final orthogonalized states and the learning trajectory seen in animals. The observed cellular and population dynamics constrain the mechanisms underlying cognitive map formation in the hippocampus, pointing to hidden state inference as a fundamental computational principle, with implications for both biological and artificial intelligence.

DOI: 10.1038/s41586-024-08548-w

Source: https://www.nature.com/articles/s41586-024-08548-w