Theoretical Neuroscience part I 2004

This is last year's course. For this year's course go to Theoretical Neuroscience part I 2005
Theoretical Neuroscience part II 2005
 
General course information including a syllabus etc. is here. Also have a look at the Gatsby teaching schedule for other courses taught by Gatsby people. This page will mainly contain the weekly assignments and some additional reading. The course is open to anybody from the University of London. If you'd like to attend, please contact Mary Hardie.
 
Lecturers  Topic (see course info for more details)
Peter Latham Biophysics, single neurones, networks
Maneesh Sahani and Liam Paninski Neural coding, Information theory
Peter Dayan Learning
 
Teaching Assistant Quentin Huys
Any questions or things you'd specifially like to cover in the revision sessions, just send Quentin an
 
Time and place Gatsby Unit room 409, 4th floor, 17 Queen Square
Lectures Tuesdays and Fridays 11am to 1pm
Revision sessions Fridays 2 - 3pm
 

The main course book is Theoretical Neuroscience by Dayan and Abbott. The appendix of the book describes the maths needed for the course.

If you need to brush up on maths one recommended book is: Riley, Hobson and Bence: Mathematical Methods for Physicists. For a very simple intro to first order ODE's, see Hugh Wilson: spikes, decisions and actions, chapter 1 and 2. A few very useful cribsheets: basic maths you'll need for this course, some matrix identities, and more matrix algebra.

Some other useful resources on the web, including other theoretical neuroscience course sites are here. You might also be interested in the Gatsby neuro journal club.


 
Date ReadingAssignment
8.10.Lecture 1
Ions, membranes, Hodgkin and Huxley
Dayan and Abbott chapter 5
For the keen:
Johnston and Wu, chapter 2, 5 and 6
Hodgin and Huxley (1952)
1, pdf
15.10.Lectures 2/3
Cable equation and synapses
Dayan and Abbott chapter 6
For the keen: Koch (1999): chapters 2-5
Cox and Gabbiani's course site
2, pdf
22.10.Lectures 4/5
Synapses and phase planes
Dayan and Abbott chapter 5/6
For the keen:
Wilson: Spikes, Decisions, Actions
Gerstner and Kistler: Spiking Neuron Models
3, pdf
29.10.Lectures 6/7
Systems neuroscience,
spike statistics
Dayan and Abbott chapter 2 on vision
Carpenter RHS: chapters on vision and hearing
More in depth: Kandel: chapters 25-31
Zigmond: chapters 21,22,27,28
Spike stats: Richard Hahnloser's notes
4, pdf
5.11.Lectures 8/9
Encoding models / spike analysis
lecture 8 slides: discrete encoding
lecture 9 slides: continuous encoding
Dayan and Abbott chapter 3/4
A review paper on LN methods
and one on point processes in neuroscience: Brown et al. 2002
5, pdf
12.11.Lectures 10/11
Decoding models / Info theory
lecture 10 slides: decoding
lecture 11 slides: estimation of information-theoretic quantities
Dayan and Abbott chapter 3/4
notes on sufficient statistics
6, pdf
19.11.Lectures 12/13
Information theory / Population coding
Dayan and Abbott chapter 3/47, pdf
26.11.Lectures 14/15
Learning / Hebb rules
Dayan and Abbott chapter 8
lecture 14/15 slides
8, pdf
3.12.Lectures 16/17/18
Dynamics of large networks
Wilson and Cowan 1972, 1973
Wilson: Spikes, Decisions, Actions
Dayan and Abbott chapter 7
9, pdf
10.12.Lecture 19
Reinforcement learning
Dayan and Abbott chapter 9
lecture 18/19 slides
 
 

 
Page maintained by Quentin Huys