9.29J / 9.912J / 8.261J Introduction to Computational Neuroscience, Spring 2004
Data from an experiment on the weakly electric fish Eigenmannia. The frequency of action potential firing increases when the stimulus increases. (Image by Prof. Sebastian Seung from his notes on neural coding: Linear models.)
課程重點
This course features a selection of downloadable lecture notes, and problem sets in the assignments section.
課程描述
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.
技術需求
Special software is required to use some of the files in this course: .mat, and .m.
