Neural Computation 2012-2013
Neural Computation (NC) is a 10 point MSc course of 18 lectures in the first semester.
(Other students can attend after agreement).
Lectures are Tuesday and Friday: 12.10-13.00, Tuesday and
Friday in LT4 7BSQ. We start on time!
Instructor: Mark van Rossum
In this course we study the computations
carried out by the nervous system.
Unlike other courses and artifical neural networks,
we take a bottom-up approach. This means that we incorporate
data from neurobiology, simulate certain aspects of it, and try to formulate
theories about the brain.
single neuron models,
neural codes, plasticity models.
Copies of the lecture notes part1
will be handed out in
Office hours: make an appointment or catch me after the
No prior biology/neuroscience knowledge is required. I use a small subset of
not very advanced math in the lectures. These older FMCS lecture
can be used as a refresher. Alternatively, use Google to refresh
forgotten maths if needed. If you are still stuck, use the practicals or office
hours to resolve the problems.
In the tutorials we use MatLab and NEURON (a
special purpose simulator). No prior experience with either is required, however
MatLab skills are valuable for many courses.
More information on
Matlab and how to make graphs and write reports.
The course will be fully assessed by two reports of practical assignments which will
appear here (deadlines will be announced). The two marks are averaged. Standard late
policies will apply. Also see How to make graphs and write reports.
Assignment 1 Deadline: 19 October, 4pm
Assignment 2 Deadline: 30 November, 4pm
Preferably hand-in hardcopy at ITO, otherwise email to mvanross@inf
Practicals are every week, AT 5.08 1-2pm.
No practicals in the first week.
Tutors are Maria Shippi and Mark van Rossum.
You can use the practicals to work on the exercises below, and ask
questions about the lecture.
Week 1; week of Sep 17
Tuesday lecture: 1. Introduction and Chapter 1: Anatomy
Friday lecture: 2. Chapter 2: Passive properties.
Week 2; week of Sep 24
Tuesday lecture: 3. Chapter 3: Hodgkin-Huxley
Friday lecture: 4. Chapter 3: Hodgkin-Huxley
Practical: 1. The NEURON simulator: Passive
Week 3; week of Oct 1
Tuesday lecture: 5. Chapter 3: Synapses
Friday lecture: 6. Chapter 4: Synapses
Practical: 2. The NEURON simulator:
Week 4; week of Oct 8
Tuesday lecture: 7. Chapter 5: Integrate and Fire
Friday lecture: 8. Chapter 6: Firing statistics
Practical: 3. NEURON: Interactions of synapses
Week 5; week of Oct 15
Tuesday lecture: 9. Chapter 7: Retina and V1
Friday lecture: no lecture
Practical: 4. Matlab: AMPA receptor
simulation. Script (will appear later): ampa.m
Week 6; week of Oct 22
Tuesday lecture: 10 Chapter 7: Retina and V1
Friday lecture: 11 Chapter 8: Coding
Practical: 5. Matlab: An Integrate and fire neuron
Week 7; week of Oct 29
Tuesday lecture: 12. Chapter 9: Higher visual processing
Friday lecture: 13. Chapter 10: Networks
Practical: Question 7 and 8 of 6. Simple and complex cells Dayan
and Abbott chapter: encode2.pdf
Week 8; week of Nov 5
Tuesday lecture: 14. Chapter 11+12: Decisions
Friday lecture: 15. Chapter 13: Hebbian Learning
Practical: 6. Matlab:
Ben-Yishai network Script: ben2.m
Week 9; week of Nov 12
Tuesday lecture: 16. Chapter 13: Hebbian Learning
Friday lecture: 17. Chapter 13: Hebbian Learning
Week 10; week of Nov 19
Tuesday lecture: 18. spill-over
Practical: 8. Matlab: Hebbian learning
with constraints Script (will appear later):
Additional material (discussed in the lectures):
Movies of LGN and V1 recordings (play with mplayer under linux):
Recurrent 6-node network with chaotic behavior bifur6.m