Course: STAT 42600=CPNS 35600, ORGB 42600
Title: Theoretical Neuroscience: Statistics and Information Theory
Instructor(s): Stephanie Palmer
Class Schedule: Sec 01: TR 9:40–11:00 AM (Remote)
Description: This course begins with an introduction to inference and statistical methods in data analysis. We then cover the two main sections of the course: I) Encoding and II) Decoding in single neurons and neural populations. The encoding section will cover receptive field analysis (STA, STC, and non-linear methods such as maximally informative dimensions) and will explore linear-nonlinear-Poisson models of neural encoding as well as generalized linear models alongside newer population coding models. The decoding section will cover basic methods for inferring stimuli or behaviors 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.
