Kobayashi Yasushi, Okada Ken-Ichi
Graduate School of Frontier Biosciences, Osaka University, 1-3 Machikaneyama, Toyonaka 560-8531, Japan.
Ann N Y Acad Sci. 2007 May;1104:310-23. doi: 10.1196/annals.1390.003. Epub 2007 Mar 7.
In this article, we address the role of neuronal activity in the pathways of the brainstem-midbrain circuit in reward and the basis for believing that this circuit provides advantages over previous reinforcement learning theory. Several lines of evidence support the reward-based learning theory proposing that midbrain dopamine (DA) neurons send a teaching signal (the reward prediction error signal) to control synaptic plasticity of the projection area. However, the underlying mechanism of where and how the reward prediction error signal is computed still remains unclear. Since the pedunculopontine tegmental nucleus (PPTN) in the brainstem is one of the strongest excitatory input sources to DA neurons, we hypothesized that the PPTN may play an important role in activating DA neurons and reinforcement learning by relaying necessary signals for reward prediction error computation to DA neurons. To investigate the involvement of the PPTN neurons in computation of reward prediction error, we used a visually guided saccade task (VGST) during recording of neuronal activity in monkeys. Here, we predict that PPTN neurons may relay the excitatory component of tonic reward prediction and phasic primary reward signals, and derive a new computational theory of the reward prediction error in DA neurons.
在本文中，我们探讨了神经元活动在脑干-中脑回路的奖赏通路中的作用，以及认为该回路比以往强化学习理论具有优势的依据。有几条证据支持基于奖赏的学习理论，该理论提出中脑多巴胺（DA）神经元发送一个教学信号（奖赏预测误差信号）来控制投射区域的突触可塑性。然而，奖赏预测误差信号在何处以及如何计算的潜在机制仍不清楚。由于脑干中的脚桥被盖核（PPTN）是DA神经元最强的兴奋性输入源之一，我们假设PPTN可能通过将奖赏预测误差计算所需的信号传递给DA神经元，在激活DA神经元和强化学习中发挥重要作用。为了研究PPTN神经元在奖赏预测误差计算中的参与情况，我们在记录猴子神经元活动期间使用了视觉引导的扫视任务（VGST）。在此，我们预测PPTN神经元可能传递紧张性奖赏预测和相位性初级奖赏信号的兴奋性成分，并推导出一种关于DA神经元奖赏预测误差的新计算理论。
