Neural Computation Unit, Okinawa Institute of Science and Technology, Okinawa 904-0412, Japan.
Curr Opin Neurobiol. 2011 Jun;21(3):368-73. doi: 10.1016/j.conb.2011.04.001. Epub 2011 Apr 29.
Accumulating evidence shows that the neural network of the cerebral cortex and the basal ganglia is critically involved in reinforcement learning. Recent studies found functional heterogeneity within the cortico-basal ganglia circuit, especially in its ventromedial to dorsolateral axis. Here we review computational issues in reinforcement learning and propose a working hypothesis on how multiple reinforcement learning algorithms are implemented in the cortico-basal ganglia circuit using different representations of states, values, and actions.
越来越多的证据表明，大脑皮层和基底神经节的神经网络在强化学习中起着至关重要的作用。最近的研究发现，皮质-基底神经节回路内存在功能异质性，特别是在其腹侧到背侧轴上。在这里，我们回顾了强化学习中的计算问题，并提出了一个工作假设，即使用状态、值和动作的不同表示形式，多个强化学习算法如何在皮质-基底神经节回路中实现。
