This graduate course will introduce computational models of information processing in mammalian central nervous system. Topics include models of single neuron, artificial neural network (connectionism), and neural organization and architecture. This term, we will place our emphasis on reinforcement learning algorithms for Markov decision framework that has been a hot topic in machine learning in recent years (temporal difference method, Q-learning, and related algorithms). Students may elect the course for 2-4 credits per arrangement with the instructor.