9.29J Introduction to Computational Neuroscience | Brain and Cognitive Sciences
This course gives a mathematical introduction to neural coding and dynamics. Topics include convolution, correlation, linear systems, game theory, signal detection theory, probability theory, information theory, and reinforcement learning. Applications to neural coding, focusing on the visual system are covered, as well as Hodgkin-Huxley and other related models of neural excitability, stochastic models of ion channels, cable theory, and models of synaptic transmission. Visit the Seung Lab Web site. Author: Seung, SebastianOrganization: Massachusetts Institute of Technology Categories: Social Sciences / Psychology / Cognitive View More Information about the Course on MERLOT View Course
Link to Full Article: 9.29J Introduction to Computational Neuroscience | Brain and Cognitive Sciences
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