THEORETICAL NEUROSCIENCE

- Gatsby Computational Neuroscience Unit, Semester 1

Theoretical neuroscience is a mathematical approach to understanding neural systems. This course provides a thorough introduction to current approaches in the field.

Heavy use will be made of mathematical, statistical, and computational methods. The course is open to anyone, although 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).

A relevant crib sheet can be found here.


The course textbook is Theoretical Neuroscience by Dayan and Abbot.


Course Materials - click here.

Homeworks - click here.

Homeworks are issued weekly (on Friday) and you have one week to complete them. Please submit your solutions during the lecture on the following Friday.


Dates: Tuesdays and Fridays, 9th Oct - 15th Dec 2009

Time: 11.00 - 13.00


Venue:

4th Floor Seminar Room

Gatsby Unit

Alexandra House, 17 Queen Square

London

WC1N 3AR


Lecturers

Peter Latham, Maneesh Sahani, and Peter Dayan.


Teaching Assistants

The TA responsibilities are being split between

Ross Williamson (rossw@gatsby.ucl.ac.uk) & David Barrett (barrett@gatsby.ucl.ac.uk).



Review Sessions:

There will be a review session every Friday, from 13.30 - 15.30.

These review sessions are largely intended to consolidate both the lecture and homework material.

This course also requires the use of  a wide variety of mathematical techniques. We are more than happy to use some of the review time to provide mathematical refreshers to those that need them.

If you have any specific questions that you would like to see addressed, please email either of the TA’s (emailing us in advance will allow us to better address your question in the review session).


Calendar

Oct 9th  -     Introduction - Peter Latham / Systems 1 - Maneesh Sahani

Oct 13th -    No Lecture

Oct 16th -    Systems 2 - Maneesh Sahani

Oct 20th -    Neural Coding 1 - Maneesh Sahani

Oct 23rd -    Neural Coding 2 - Maneesh Sahani

Oct 27th -    Neural Coding 3 - Maneesh Sahani

Oct 30th -    Neural Coding 4 - Maneesh Sahani

Nov 3rd  -    Neural Coding 5 - Maneesh Sahani

Nov 6th  -    Information Theory - Maneesh Sahani

Nov 10th -   Biophysics 1 - Peter Latham

Nov 13th -   Biophysics 2 - Peter Latham

Nov 17th -   Biophysics 3 - Peter Latham

Nov 20th -   Biophysics 4 - Peter Latham

Nov 24th -   Learning - Peter Dayan

Nov 27th -   Decision Making - Peter Dayan

Dec 1st  -     Reinforcement Learning - Peter Dayan

Dec 4th  -     Network Dynamics 1 - Peter Latham

Dec 8th  -     Network Dynamics 2 - Peter Latham

Dec 11th -    Network Dynamics 3 - Peter Latham

Dec 15th -    Uncertainty - Peter Dayan