Neural Computation 2011-2012

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 in AT LT1 and Friday in AT2.14. 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 part1, part2 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 Deadline: Fri 4 Nov, 4pm.
Additional file: noisy_iclamp_mvr.mod
Notes on the solution

Assignment 2 Deadline: Monday 5 Dec, 4pm.

Additional file: Matlab/Octave code

Practicals

Practicals are every Wednesday 1-3pm in AT 5.06. No practicals in the first week. Tutors are Paolo Puggioni and Rui Costa.

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: NO LECTURE
Friday lecture: NO LECTURE
Practical: 2. The NEURON simulator: Hodgkin-Huxley model

Week 4; week of Oct 10
Tuesday lecture: 5. Chapter 3: Synapses
Friday lecture: 6. Chapter 4: Synapses
Practical: 3. NEURON: Interactions of synapses on dendrites

Week 5; week of Oct 17
Tuesday lecture: 7. Chapter 5: Integrate and Fire
Friday lecture: 8. Chapter 6: Firing statistics
Practical: Practical: 4. Matlab: AMPA receptor simulation. Script (will appear later): ampa.m

Week 6; week of Oct 24
Tuesday lecture: 9. Chapter 7: Retina and V1
Friday lecture: 10. Chapter 7: Retina and V1
Practical: Matlab: An Integrate and fire neuron Script: mvr_if_matlab.m

Week 7; week of Oct 31
Tuesday lecture: 11. Chapter 8: Population codes
Friday lecture: 12. Chapter 8: Coding
No practical

Week 8; week of Nov 7
Tuesday lecture: NO LECTURE
Friday lecture: NO LECTURE
No practical

Week 9; week of Nov 14
Tuesday lecture: 13. Chapter 9: Higher visual processing
Friday lecture: 14. Chapter 10: Networks
Practical: 5. Matlab: Mutual information of a Poisson spiker Script (will appear later): bits.m

Week 10; week of Nov 21
Tuesday lecture: 15. Chapter 11+12: Decisions
Friday lecture: 16. Chapter 13: Hebbian Learning
Practical: 6. Matlab: Ben-Yishai network Script: ben2.m

Additional information:

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

Home : Teaching : Courses