Neural Computation 2018-2019

Neural Computation (NC) is a 10 point course of 18 lectures in the first semester. It is suitable for 4th year undergraduate students and MSc students.

If you are interested but unsure if you can attend, please contact the course lecturers.

Lectures will at 12.10 on Tuesday and Friday in David Hume Tower, room LG.11

The first lecture will be in week 1 on September 18th 2018.

Instructors: Matthias Hennig and Peggy Series

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 neurobiological research and pitfalls in modelling real-world systems.

For whom is this course?
This course should 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. These links are explored in more detail in the NIP course, for which this course is a good preparation. It should also be noted that the course has limited direct practical applicability outside academic research.

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

There are extensive Lecture notes , during the lectures we go linearly through them. As for any course, preparatory reading of the notes before each lecture is recommended.

Office hours: make an appointment via email or catch us after the lecture.


No prior biology/neuroscience knowledge is required. We 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/Octave 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 (non-assessed). Deadline: Oct 18 - Required file: noisy_iclamp.mod
Assignment 2 (assessed). Deadline: Nov 29


Practicals are every week, starting week 2. Time and location: 10.10-11.00 in AT 5.05 - West Lab. You can use the practicals to work on the exercises below, and ask questions about the lectures. Attendance is not compulsory, but highly recommended.

Timetable (approximate, see the Learn page for up to date info)

Week 1; week of Sep 17
Tuesday lecture: 1. Introduction and Chapter 1: Anatomy.
Friday lecture: 2. Chapter 2: Passive properties.
No practical.

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 properties

Week 3; week of Oct 1

6. Chapter 4: Synapses
Practical: 2. The NEURON simulator: Hodgkin-Huxley model

Week 4; week of Oct 8
7. Chapter 5: Integrate and Fire models
Practical: 3. Matlab: AMPA receptor simulation. Script: ampa.m

Week 5; week of Oct 15
Tuesday lecture: 8. Chapter 6: Firing statistics
Friday lecture: 9. Chapter 7: Retina and LGN
Practical: 4. Matlab: An Integrate and fire neuron  Script: NClab4.m

Week 6; week of Oct 22
Tuesday lecture: 11 Chapter 8: Higher visual processing
Friday lecture: 12. Chapter 9: Coding
Practical: 5. Simple and complex cells in V1 (based on question 7 and 8 of Dayan and Abbot , accompanying Dayan and Abbott chapter: encode2.pdf)

Week 7; week of Oct 29

14. Chapter 11: Spiking networks
Practical: 6. Simple and complex cells in V1 (based on question 7 and 8 of Dayan and Abbot , plus temporal response, accompanying Dayan and Abbott chapter: encode2.pdf)

Week 8; week of Nov 5
Tuesday lecture: 15. Chapter 13: Hebbian Learning
Friday lecture: 15. Chapter 13: Hebbian Learning
Practical: 7. Information in spike trains.

Week 9; week of Nov 12
Tuesday lecture: 16. Chapter 13: Spike timing dep. Hebbian Learning
Practical: 8. Matlab: Ben-Yishai network.

Week 10; week of Nov 19
Tuesday lecture: 17. Learning and memory
Friday lecture: 17. Learning and memory

Practical: 9. Matlab: Hebbian learning.

Week 11; week of Nov 26
Tuesday: no lecture
Friday lecture: Guest lecture: Barbara Webb on Modelling the neural basis of insect navigation.
Lab: tbd

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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