PHY 687 -- Topics in biological physics

Introduction to Computational Neuroscience

THIS IS NOT A BREADTH COURSE.
1 CREDIT.
SCHEDULE: TUE 2:20-3:40.

This is a class on computational neuroscience... with a theoretical physics spin. I will present a selection of few topics, but dig very deep in each of them to extract some cool results. I'll try to keep it self-contained and give a quick introduction to some of the technical tools needed along the way. Even so, this class is meant for students who can manage some elementary physics and maths: some knowledge of statistical mechanics would be very useful, but I'll give a recap of that anyway; same with probability theory and inference.

Syllabus

Class: Tue 2:20am-3:40pm in Physics B131

Instructor: Luca Mazzucato

Office Hours: Come say hi at my office SCGP 506 or drop me an email: lmazzucato at scgp dot stonybrook dot edu.

Lecture Notes:

Notes updated on November 6th 2011 (update supervised and unsupervised learning chapters).

Useful Texts

Hertz, Krogh & Palmer, Introduction to the theory of neural computation The Hopfield network and the supervised learning part of the class are partly based on this book.
David MacKay, Information Theory, Inference, and Learning Algorithms. Learn while having fun! Fantastic book, and it's freely available online. The inference and the Boltzmann machine parts of the course are based on this book. It's going to become your favorite book, resistance is futile.
Mezard, Parisi & Virasoro, Spin glass theory and beyond. Classic statistical mechanics book for the theory of spin glasses and the Hopfield network. Includes reprints of all the original papers, a must-read for everybody. I can't stress this enough: you will really enjoy reading the original papers. Back then, people put a lot of efforts in writing clear papers...
Amit, Modeling brain functions. An exhaustive treatment of attractor neural networks as of the early nineties, starting from the Hopfield model. If you are looking for a more didascalic approach than the previous book.
Abbott & Dayan, Theoretical neuroscience. The main textbook to get acquainted with the field of neuroscience. You can't live without it.
Trappenberg, Fundamentals of computational neuroscience. Textbook in computational neuroscience. Parallel to the explanations of each topic, you can find Matlab code examples to start your own neural network simulations. Super fun.
Dalvit et al., Problems on statistical mechanics. Collection of exercises on statistical mechanics, with solutions. Very useful as a general recap, explains many cool tricks to compute partition functions.

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