Introduction to Computational Neuroscience (2004)

Spring 2004 schedule: Tuesday and Thursday 11 - 12:30 in E51-085

Mathematical introduction to neural coding and dynamics. 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. Hodgkin-Huxley and related models of neural excitability, stochastic models of ion channels, cable theory, and models of synaptic transmission.

Prof. Sebastian Seung, seung@mit.edu
Office hours: TBA, E25-429

T.A.s Jennifer Wang (jenwang@mit.edu) and Justin Werfel (jkwerfel@mit.edu)
Office hours: Tuesday, 4pm-5pm E25-425 (Jen) or by appointment

Optional lectures will be Mondays 7-8 PM in selected weeks, held in E25-401.

No optional lecture this week (3/8)

Lectures

Homework assignments

Midterm projects

Final projects

Resources

Philosophy

The central assumption of computational neuroscience is that the brain computes. What does that mean? Generally speaking, a computer is a dynamical system whose state variables encode information about the external world. In short, computation equals coding plus dynamics. Some neuroscientists study the way that information is encoded in neural activity and other dynamical variables of the brain. Others try to characterize how these dynamical variables evolve with time. The study of neural dynamics can be further subdivided into two separate strands. One tradition, exemplified by the work of Hodgkin and Huxley, focuses on the biophysics of single neurons. The other focuses on the dynamics of networks, concerning itself with phenomena that emerge from the interactions between neurons. Therefore computational neuroscience can be divided into three subspecialties: neural coding, biophysics of neurons, and neural networks.

Prerequisites

basic biology, chemistry, and physics
differential equations or permission of instructor. Linear algebra is also desirable.
knowledge of MATLAB or willingness to learn. For more information see this MIT website
Course requirements

weekly problem sets

midterm project

final project

Textbook

Peter Dayan and Larry Abbott, Theoretical Neuroscience. We will follow the first six chapters of the book very closely, and the later chapters more sketchily.