Doya Kenji
ATR Human Information Science Laboratories, CREST, Japan Science and Technology Corporation, Kyoto.
Neural Netw. 2002 Jun-Jul;15(4-6):495-506. doi: 10.1016/s0893-6080(02)00044-8.
This paper presents a computational theory on the roles of the ascending neuromodulatory systems from the viewpoint that they mediate the global signals that regulate the distributed learning mechanisms in the brain. Based on the review of experimental data and theoretical models, it is proposed that dopamine signals the error in reward prediction, serotonin controls the time scale of reward prediction, noradrenaline controls the randomness in action selection, and acetylcholine controls the speed of memory update. The possible interactions between those neuromodulators and the environment are predicted on the basis of computational theory of metalearning.
本文从介导调节大脑中分布式学习机制的全局信号这一观点出发，提出了一种关于上行神经调节系统作用的计算理论。基于对实验数据和理论模型的综述，我们提出多巴胺信号表示奖励预测中的误差，血清素控制奖励预测的时间尺度，去甲肾上腺素控制动作选择的随机性，而乙酰胆碱控制记忆更新的速度。基于元学习的计算理论，预测了这些神经调节剂与环境之间可能的相互作用。
