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

Short description

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.

Keywords: single neuron models, neural codes, plasticity models.

Copies of the lecture notes part1, part2 will be handed out in the lectures.

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

Prerequisites

No prior biology/neuroscience knowledge is required. I use a small subset of not very advanced math in the lectures. 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.

Assessement

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

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.

Timetable (approximate)

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

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 on dendrites

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 Script: mvr_if_matlab.m

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): hebb.m

Additional material (discussed in the lectures):

Movies of LGN and V1 recordings (play with mplayer under linux):

hubel_Wiesel_lgn_off_cell.asf
hubel_wiesel_binocular_cell.asf
hubel_wiesel_complex.asf
hubel_wiesel_directional_cell.asf
hubel_wiesel_lgn_on_cell.asf
hubel_wiesel_simple_cell.asf

Recurrent 6-node network with chaotic behavior bifur6.m

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