Neural Computation 2008-2009

Neural Computation (NC) is a 10 point MSc course of 18 lectures.
Lectures are Tuesday and Friday: 10.00-10.50, both in FH1.B01 (Forrest Hill). 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 aritifical 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 (pdf) will be handed out in the lectures. Many of the cited references can be found using PubMed

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 and Script files . Deadline: Friday March 6, 15.00. Hand in to ITO

Assignment 2 out since Friday Mar 27. Deadline: Friday April 17th, 15.00. Hand in to ITO

Practicals

Time and place of the practicals are to be determined. No practicals in the first week. Tutors are Seymour Knowles-Barley and Cian O'Donnell.

Timetable

Week 1; week of Jan 12
Tuesday lecture: Introduction and Chapter 1: Anatomy
Friday lecture: Chapter 2: Passive properties.
No practical.

Week 2; week of Jan 19
Tuesday lecture: Chapter 3: Hodgkin-Huxley
Friday lecture: Chapter 3: Hodgkin-Huxley
Wednesday Practical: 1. The NEURON simulator: Passive properties

Week 3; week of Jan 26
Tuesday lecture: Chapter 4: Synapses
Friday lecture: Chapter 4: Synapses
Wednesday Practical: 2. The NEURON simulator: Hodgkin-Huxley model

Week 4; week of Feb 2
Tuesday lecture: Chapter 5: Integrate and Fire
Friday lecture: Chapter 6: Firing statistics
Wednesday Practical: Cancelled.

Week 5; week of Feb 9
Tuesday lecture: NO LECTURE
Friday lecture: Chapter 7: Retina and V1
Wednesday Practical: 3. NEURON: Interactions of synapses on dendrites

Week 6; week of Feb 16
Tuesday lecture: Chapter 7: Retina and V1
Friday lecture: Chapter 8: Population codes
Wednesday Practical: 4. Matlab: AMPA receptor simulation. Script (will appear later): ampa.m

Week 7; week of Feb 23
Tuesday lecture: Chapter 8: Coding
Friday lecture: Chapter 9: Higher visual processing
Wednesday Practical: 5. Matlab: Mutual information of a Poisson spiker Script (will appear later): bits.m

Week 8; week of Mar 2
Tuesday lecture: Chapter 10: Networks
Friday lecture: Chapter 11+12: Decisions
Wednesday Practical: 6. Matlab: Ben-Yishai network Script: ben2.m

Week 9; week of Mar 9
Tuesday lecture: NO LECTURE
Friday lecture: Chapter 13: Hebbian Learning
Wednesday Practical: 7. Matlab: Hebbian learning with constraints Script (will appear later): hebb.m

Week 10; week of Mar 16
Tuesday lecture: Chapter 14: Spike timing dependent plasticity
Friday lecture:
No practical.

Additional information:

Additional Matlab: An Integrate and fire neuron Script: mvr_if_matlab.m

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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