麦吉尔大学Mark P. Brandon课题组取得一项新突破。他们的研究揭示了海马体中奖励的预测编码。相关论文于2026年1月14日发表在《自然》杂志上。

在这里，研究组在几周内跟踪了老鼠学习解决认知要求高的奖励任务时海马体奖励表征的演变。该研究组在群体和单细胞水平上发现了几条线索，表明当母细胞在几周内学习任务时，海马体的表征会预测奖励。群体水平的奖励编码和奖励调节神经元的比例都随着经验的增加而减少。与此同时，在奖励之前的特征的表征随着经验的增加而增加。随着时间的推移，通过跟踪奖励调谐神经元，课题组发现它们的活动逐渐从编码奖励本身转变为代表先前的任务特征，这表明经验驱动了神经活动的向后转移重组，以预测奖励。课题组人员表明，地点场的时间差异模型11概括了这些结果。他们的发现强调了海马体表征的动态本质，并强调了它们在通过预测未来结果进行学习中的作用。

据了解，预测未来的结果是大脑的一项基本任务。这个过程需要学习世界的状态以及这些状态之间的过渡关系。在啮齿类动物中，海马体空间认知图被认为是这样一种内部模型。然而，海马神经元表征中的预测性编码和奖励敏感性的证据表明，其作用超出了纯粹的空间表征。这种奖励表征是如何在长期经验中进化的，目前还不清楚。

附：英文原文

Title: Predictive coding of reward in the hippocampus

Author: Yaghoubi, Mohammad, Kumar, M. Ganesh, Nieto-Posadas, Andres, Mosser, Coralie-Anne, Gisiger, Thomas, Wilson, mmanuel, Pehlevan, Cengiz, Williams, Sylvain, Brandon, Mark P.

Issue&Volume: 2026-01-14

Abstract: Anticipating future outcomes is a fundamental task of the brain1,2,3. This process requires learning the states of the world as well as the transitional relationships between those states. In rodents, the hippocampal spatial cognitive map is thought to be one such internal model4. However, evidence for predictive coding5,6 and reward sensitivity7,8,9,10 in the hippocampal neuronal representation suggests that its role extends beyond purely spatial representation. How this reward representation evolves over extended experience remains unclear. Here we track the evolution of the hippocampal reward representation over weeks as mice learn to solve a cognitively demanding reward-based task. We find several lines of evidence, both at the population and the single-cell level, indicating that the hippocampal representation becomes predictive of reward as the mouse learns the task over several weeks. Both the population-level encoding of reward and the proportion of reward-tuned neurons decrease with experience. At the same time, the representation of features that precede the reward increases with experience. By tracking reward-tuned neurons over time, we find that their activity gradually shifts from encoding the reward itself to representing preceding task features, indicating that experience drives a backward-shifted reorganization of neural activity to anticipate reward. We show that a temporal difference model of place fields11 recapitulates these results. Our findings underscore the dynamic nature of hippocampal representations, and highlight their role in learning through the prediction of future outcomes.

DOI: 10.1038/s41586-025-09958-0

Source: https://www.nature.com/articles/s41586-025-09958-0