Note that for the 2016-17 academic year, this module is running in DAMTP and so the information below may not be accurate.
timing: Lent term, 16 lectures [see also lecture schedule]
assessment: 100% coursework
recommended: 3G2 or 3G3
see official syllabus at the Teaching Office website
The course demonstrates how mathematical analysis and ideas from engineering-related disciplines (dynamical systems, signal processing, machine learning, optimal control, and probabilistic inference) can be applied to gain insight into the workings of the nervous system. The course highlights a number of real-world computational problems that need to be tackled by any 'intelligent’ system, as well as the solutions that biology offers to some of these problems. The treatment is fairly mathematical and the coursework involves writing and running programs to gain hands-on experience with the subject.
Module leader | Additional lecturers | ||
---|---|---|---|
Dr. Máté Lengyel | Dr. David Barrett | Dr. Guillaume Hennequin | Dr. Rich Turner |
lecture material marked with a # are from previous years, and are continuously updated with material from this year's course
Date | Day | Lecturer | Topic (tentative) | Relevant reading* | lecture materials |
---|---|---|---|---|---|
Jan 15 | Fri | Lengyel | introduction, neural coding | introduction slides | |
Jan 20 | Wed | Barrett | networks | Ch 7 | networks slides, part 1 |
Jan 22 | Fri | Barrett | networks | Ch 7 | networks slides, part 2 |
Jan 27 | Wed | Hennequin | E-I balance | Ch 7 | excitation/inhibition balance slides |
Jan 29 | Fri | Hennequin | E-I balance | Ch 7 | |
Feb 3 | Wed | Lengyel | neural encoding | Ch 1 | neural coding slides neural encoding slides |
Feb 5 | Fri | Lengyel | neural encoding | Ch 2 | |
Feb 10 | Wed | Lengyel | neural decoding | Ch 3 | neural decoding slides |
Feb 12 | Fri | Lengyel | neural decoding | Ch 3 | |
Feb 17 | Wed | Lengyel | autoassociative memory | Ch 7-8 | associative memories slides |
Feb 19 | Fri | Lengyel | autoassociative memory | Ch 7-8 | |
Feb 24 | Wed | Turner | representational learning | Ch 10 | visual cortex and natural image statistics slides |
Feb 26 | Fri | Turner | representational learning | Ch 10 | |
Mar 2 | Wed | Turner | representational learning | Ch 10 | |
Mar 4 | Fri | Hennequin | plasticity | Ch 8 Kempter et al, 1999 | plasticity slides |
Mar 9 | Wed | Hennequin | plasticity | Ch 8 |
* chapter numbers are from Dayan & Abbott
Location
Wednesdays 12.00-13.00, CUED LR6
Fridays 11.00-12.00, CUED LR6
for directions to find the Department, see here
for directions within the Department, see floor plan
Content
Coursework will mostly involve programming exercises applying theoretical ideas taught during lectures in practice. Some of the exercises may require dealing with preprocessed data sets or code which will be provided in MatLab. Nevertheless, students are free to choose their favourite programming language to solve the exercises. A useful source of examples for the kind of exercises to be expected and for good MatLab coding practice is the exercises of the Dayan & Abbott book available online. Simulation results (figures plus brief verbal descriptions) will need to be submitted with a summary of the main methodological steps involved in obtaining the results.
Also, do not forget to:
Calendar
1st assignment network dynamics, neural coding download the assignment | 2nd assignment memory, representational learning download the assignment download the Hopfield 1982 paper download the data file representational.zip | ||
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Feb 10 | assigned | ||
Feb 25 | hand in | ||
Mar 7 | feedback | ||
Mar 9 | assigned | Lent ends: Mar 11 | |
Apr 22 | hand in | Easter starts: Apr 19 | |
May 11 | feedback |
Handing in
Dave Gautrey, Group G Administrator, EIETL post box, 1st floor, CUED Inglis Building (see floor plan for location), before 4pm
penalty for lateness is 20% of the marks available per week started that the work is late (non-negotiable)
More information about coursework may be be posted here as the course proceeds.
This list is meant as a 'menu' for major topics in computational neuroscience. In any year, for obvious practical reasons, lectures will only actually cover a select subset of these topics (see lecture schedule for details).
Principles of computational neuroscience (Dr. M. Lengyel)
Neural network dynamics (Dr. D. Barrett, Dr. G. Hennequin)
Synaptic plasticity and unsupervised learning (Dr. G. Hennequin)
Representational learning (Dr. R. Turner)
Main text book
Dayan & Abbott. Theoretical Neuroscience. MIT Press, 2005.
useful exercises with sample data and MatLab code: here
Additional reading
Rieke et al. Spikes. Exploring the Neural Code. MIT Press, 1999.
Gerstner & Kistler. Spiking Neural Networks. Cambridge University Press, 2002.
Rao et al. Probabilistic Models of the Brain: Perception and Neural Function. MIT Press, 2002.
Further reading of potential interest
Izhikevich. Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting. MIT Press, 2006.
MacKay. Information Theory, Inference & Learning Algorithms. Cambridge University Press, 2002.
Sutton & Barto. Reinforcement Learning: An Introduction. MIT Press, 1998.
O'Reilly et al. Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain. MIT Press, 2000.