Neural Computation 2016-2017

Neural Computation (NC) is a 10 point MSc course of 18 lectures in the first semester.
(Other students can attend after agreement).
Lectures will be in Appleton Tower rm 2.14 at 12.10. We start on time!
The first lecture will be in week 1 on September 20th 2016.

Instructor: Mark van Rossum
No lectures on: Oct 4, Oct 21, Nov 11, and Nov 15.

Short description

In this course we study the computations carried out by the nervous system. Unlike most courses and artificial intelligence, 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.
Apart from learning about the brain, you will also learn about numerical modelling of differential equations, non-linear dynamics, current neurbiological research and pitfalls in modelling real-world systems.

For whom is this course?
This course will appeal to students who are interested in the basic principles and 'biological hardware' implementations of computation
in human and animals brains.

For whom is it not?
The topics discussed in the course have inspired many machine learning solutions to real-life problems, however, we shall hardly
discuss those. It should also be noted that the course has little direct practical applicability outside academic research.

Keywords: single neuron models, small networks, neural codes, models of learning and synaptic plasticity.

Lecture notes part1, and part2

Office hours: make an appointment or catch me after the lecture.


No prior biology/neuroscience knowledge is required. I use a small subset of not very advanced math in the lectures. Keywords: linear differential equations, eigenvectors, Fourier transformations, fixed points. These older FMCS lecture notes 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 assessed by one piece of coursework and an exam. There will also be a formative (non-assessed) coursework. Standard late policies will apply. Also see How to make graphs and write reports.

Assignment 1. Question 1-3 of practical 4 (Ampa receptor) (non-assessed). Deadline: Oct 28th, 4pm
Note to answers assignment 1

Assignment 2 Deadline: Dec 2nd, 4pm
Note to answers assignment 2

Preferably hand-in hardcopy at ITO, otherwise email to mvanross@inf


Practicals are every week, starting week 2. Time and location: Every Wednesday 9AM, FH 1.B32 No practicals in the first weeks. You can use the practicals to work on the exercises below, and ask questions about the lectures. Attendance is not obligatory.

Timetable (approximate)

Week 1; week of Sep 19
Tuesday lecture: 1. Introduction and Chapter 1: Anatomy 
Friday lecture: 2. Chapter 2: Passive properties.
No practical.

Week 2; week of Sep 26
Tuesday lecture: 3. Chapter 3: Hodgkin-Huxley
Friday lecture: 4. Chapter 3: Hodgkin-Huxley
Practical: 1. The NEURON simulator: Passive properties

Week 3; week of Oct 3
Tuesday lecture:  5. Chapter 4: Synapses
Friday lecture:     6. Chapter 4: Synapses
Practical: 2. The NEURON simulator: Hodgkin-Huxley model

Week 4; week of Oct 10
Tuesday lecture: 7. Chapter 5: Integrate and Fire models
Friday lecture: 8. Chapter 6: Firing statistics
Practical: 4. Matlab: AMPA receptor simulation. Script: ampa.m

Week 5; week of Oct 17
Tuesday lecture: 9. Chapter 7: Retina and LGN
NO Friday lecture:
Practical: 5. Matlab: An Integrate and fire neuron  Script: mvr_if_matlab.m

Week 6; week of Oct 24
Tuesday lecture: 11 Chapter 8: Higher visual processing
Friday lecture: 12. Chapter 9: Coding

Week 7; week of Oct 31
Tuesday lecture: 13. Chapter 10: Networks
Friday lecture: 14. Chapter 11: Spiking networks
Practical: Question 7 and 8 of 6. Simple and complex cells (accompanying Dayan and Abbott chapter: encode2.pdf)

Week 8; week of Nov 7
NO Tuesday lecture:
Friday lecture: 15. Chapter 13: Hebbian Learning
Practical: 7. Matlab: Ben-Yishai network Script: ben2.m

Week 9; week of Nov 14
Tuesday lecture: 16. Chapter 13: Spike timing dep. Hebbian Learning
Friday lecture: 17. Chap 12: Decisions
Practical: NO Practical

Week 10; week of Nov 21
Tuesday lecture: 17. Chapter 13: Spike timing dep. Hebbian Learning
Friday lecture: Spill over. Practical: 8. Matlab: Hebbian learning with constraints Script (will appear later): hebb.m

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

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