Theoretical Neuroscience I (2005)

Course materials & Assignments

Dates Tuesdays & Fridays
4 October - 16 December 2005
Time 11:00-13:00
Lecturers Peter Latham, Maneesh Sahani, Peter Dayan, Jonathan Pillow, Li Zhaoping
TA Misha Ahrens
Location 4th Floor Seminar Room, Alexandra House, 17 Queen Square, London [Please see map]
About the course

This course provides an introduction to neuroscience from a computational perspective. The emphasis is on mathematical and information processing models of the brain at a range of levels (synapses to behavior) and timescales (milliseconds to days). Topics include biophysics of single neurons, synapses, dendrites and axons; neural coding at the single cell and population level; dynamics of large networks, including computing with population codes; and learning at the systems and behavioral levels.

The course is run primarily for new Gatsby students for whom it is mandatory. Students, postdocs and faculty from outside the unit are welcome to attend, but should note that the course carries no formal course unit value. It is, however, suitable for Skills Development credit as required by the Research Councils; see here more for details.

There are no formal prerequisites for the course. However, we will be making heavy use of mathematical, statistical and computational methods. Students should feel comfortable with linear algebra, ordinary differential equations, and probability theory at the level found in Boas (Mathematical Methods in the Physical Sciences) or Arfken (Mathematical Methods for Physicists).

Most of the course material will be drawn from the textbook "Theoretical Neuroscience" by Peter Dayan & Larry Abbott (MIT Press, ISBN 0-262-04199-5) unless specified otherwise.

Lecture schedule
4/10: membranes 7/10: axons and dendrites
11/10: synapses 14/10: overview of neural systems
18/10: spike train statistics 21/10: information theory
25/10: information theory 28/10: single cell coding
1/11: single cell coding 4/11: population coding
8/11: population coding 11/11: hebbian learning
15/11: reinforcement learning 18/10: reinforcement learning
22/11: dynamical analysis 25/11: network dynamics
29/11: network dynamics    
    2/12: cortical dynamics and vision
6/12: no lecture 9/12: no lecture
13/12: representational learning 16/12: tba
To attend Please contact the unit.