Rapid phasic activity of midbrain dopamine neurons is thought to signal reward prediction errors (RPEs), resembling temporal difference errors used in machine learning. However, recent studies describing slowly increasing dopamine signals have instead proposed that they represent state values and arise independent from somatic spiking activity. Here we developed experimental paradigms using virtual reality that disambiguate RPEs from values. We examined dopamine circuit activity at various stages, including somatic spiking, calcium signals at somata and axons, and striatal dopamine concentrations. Our results demonstrate that ramping dopamine signals are consistent with RPEs rather than value, and this ramping is observed at all stages examined. Ramping dopamine signals can be driven by a dynamic stimulus that indicates a gradual approach to a reward. We provide a unified computational understanding of rapid phasic and slowly ramping dopamine signals: dopamine neurons perform a derivative-like computation over values on a moment-by-moment basis.

中脑多巴胺神经元的快速阶段性活动被认为是奖励预测错误（RPE）的信号，类似于机器学习中使用的时间差错误。然而，最近描述缓慢增加的多巴胺信号的研究提出，它们代表状态值并且独立于体细胞尖峰活动而产生。在这里，我们使用虚拟现实开发了实验范式，消除了 RPE 与价值观的歧义。我们检查了不同阶段的多巴胺回路活动，包括体细胞尖峰、体细胞和轴突的钙信号以及纹状体多巴胺浓度。我们的结果表明，多巴胺信号的变化与 RPE 一致，而不是与数值一致，并且在检查的所有阶段都观察到这种变化。多巴胺信号的变化可以由动态刺激驱动，表明逐渐接近奖励。我们提供了对快速相位和缓慢斜坡多巴胺信号的统一计算理解：多巴胺神经元逐时对值执行类似导数的计算。