美国普林斯顿神经科学研究所Nathaniel D. Daw和Ilana B. Witten研究组发现特定特征预测误差模型可解释多巴胺（DA）能异质性。2024年7月3日，国际学术期刊《自然-神经科学》发表了这一成果。

虽然奖赏预测误差（RPE）模型的主要扩展系列在多个并行电路中复制了经典模型，但研究认为这些模型并不适合解释有关DA神经元任务变量编码异质性的报告。相反，研究人员引入了一个互补的 "特异性RPE"模型，假设单个腹侧被盖区DA神经元针对动物瞬间情况的不同方面产生RPE。

此外，研究还展示了如何扩展该框架，以解释黑质紧密区DA神经元报告反应的异质性模式。这一理论调和了对DA异质性的新观察和有关RPE编码的经典观点，同时也为大脑如何在高维环境中进行强化学习提供了一个新的视角。

据介绍，中脑多巴胺神经元指示RPE这一假说是计算神经科学的伟大成就之一。然而，最近的研究结果却与这一理论的核心内容相矛盾：具体来说，神经元传递的是一种标量、同质的信号。

附：英文原文

Title: A feature-specific prediction error model explains dopaminergic heterogeneity

Author: Lee, Rachel S., Sagiv, Yotam, Engelhard, Ben, Witten, Ilana B., Daw, Nathaniel D.

Issue&Volume: 2024-07-03

Abstract: The hypothesis that midbrain dopamine (DA) neurons broadcast a reward prediction error (RPE) is among the great successes of computational neuroscience. However, recent results contradict a core aspect of this theory: specifically that the neurons convey a scalar, homogeneous signal. While the predominant family of extensions to the RPE model replicates the classic model in multiple parallel circuits, we argue that these models are ill suited to explain reports of heterogeneity in task variable encoding across DA neurons. Instead, we introduce a complementary ‘feature-specific RPE’ model, positing that individual ventral tegmental area DA neurons report RPEs for different aspects of an animal’s moment-to-moment situation. Further, we show how our framework can be extended to explain patterns of heterogeneity in action responses reported among substantia nigra pars compacta DA neurons. This theory reconciles new observations of DA heterogeneity with classic ideas about RPE coding while also providing a new perspective of how the brain performs reinforcement learning in high-dimensional environments.

DOI: 10.1038/s41593-024-01689-1

Source: https://www.nature.com/articles/s41593-024-01689-1