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Course: STAT 42600=CPNS 35600
Title: Theoretical Neuroscience: Statistics & Information Theory
Instructor(s): Stephanie Palmer
Class Schedule: Sec 01: TR 9:00–10:20AM
Office Hours:  
Description: This course will cover topics in statistics and information theory as applied to neural modeling and data.  The course begins with the spike sorting problem, used as an introduction to inference and statistical methods in data analysis.  We then begin the two main sections of the course:  I) Encoding and II) Decoding in single neurons and populations.  The encoding section will cover receptive field analysis (STA, STC and non-linear methods such as maximally informative dimensions) and will explore LNP models of neural encoding as well as generalized linear models and newer population coding models.  The decoding section will cover basic methods for inferring the stimulus from spike train data, including both linear and correlational approaches to population decoding.  The course will use examples from real data (where appropriate) in the problem sets which students will solve using Matlab.
Suggested Reading: Cover, T. M., & Thomas, J. A. (2006). Elements of information theory. Wiley-interscience.

Abbott, L. F., & Dayan, P. (2001). Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT press.

Rieke, F., Warland, D., van Steveninck, R. D. R., & Bialek, W. (1999). Spikes: exploring the neural code. MIT press.