Postgraduate Course: Neural Computation (INFR11008)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Course type | Standard |
Availability | Available to all students |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Credits | 10 |
| Home subject area | Informatics |
Other subject area | None |
| Course website |
http://course.inf.ed.ac.uk/nc |
Taught in Gaelic? | No |
| Course description | This module aims to examine:
How the brain computes and processes information from the outside world.
How the brain wires up and how it stores information.
We will study the brain at a fairly low level, so that we can make contact with neurophysiological data. We will show the necessary biological data and how it can be described in mathematical terms. We will present modelling methods applicable to various levels of organisation of the nervous system (e.g. single cells, networks of cells). We discuss models of particular brain subsystems.
In the practical session we use Matlab and NEURON to simulate the models (No familiarity with NEURON required, some self study of Matlab is beneficial.) |
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser.
Experience in programming or simulation systems desirable. Fair amount of mathematics (first order differential equations, eigenvectors, descriptive statistics). No background in Neuroscience is necessary. |
| Additional Costs | None |
Information for Visiting Students
| Pre-requisites | None |
| Displayed in Visiting Students Prospectus? | Yes |
Course Delivery Information
|
| Delivery period: 2013/14 Semester 1, Available to all students (SV1)
|
Learn enabled: No |
Quota: None |
|
Web Timetable |
Web Timetable |
| Course Start Date |
16/09/2013 |
| Breakdown of Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 20,
Supervised Practical/Workshop/Studio Hours 20,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
58 )
|
| Additional Notes |
|
| Breakdown of Assessment Methods (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| No Exam Information |
|
| Delivery period: 2013/14 Semester 1, Part-year visiting students only (VV1)
|
Learn enabled: No |
Quota: None |
|
Web Timetable |
Web Timetable |
| Course Start Date |
16/09/2013 |
| Breakdown of Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 20,
Supervised Practical/Workshop/Studio Hours 10,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
68 )
|
| Additional Notes |
|
| Breakdown of Assessment Methods (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| No Exam Information |
Summary of Intended Learning Outcomes
1 - Demonstrate a basic knowledge of neuroscience and neural computation.
2 - Abstract neuroscience experimental data to a model and should be able to critically evaluate these models.
3 - Understand the major limitations in verifying the model experimentally. |
Assessment Information
Written Examination 0
Assessed Assignments 100
Oral Presentations 0
Assessment
There will be two assessed assignments.
If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year. |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
*Introduction and overview of the brain
*The neuron
*Biophysical and reduced models of neurons
*Synapses
*Computation and coding in the brain
*Networks of neurons
*Early and higher visual processing
*Network-level modelling
*Plasticity and learning
Relevant QAA Computing Curriculum Sections: Simulation and Modelling, Artificial intelligence |
| Transferable skills |
Not entered |
| Reading list |
* Supplementary reading list below (Detailed lecture notes are provided)
* Shepherd, G. M. (1994). Neurobiology. Oxford University Press, New York, third edition.
* Abbott and Dayan (2001) Theoretical Neuroscience . MIT press (recommended)
* Koch, C. and Segev, I., editors (1998). Methods in Neuronal Modelling: From Ions to Networks. MIT Press, Cambridge, Massachusetts, second edition.
* Churchland, P. S. and Sejnowski, T. J. (1992). The Computational Brain. MIT Press, Cambridge, Massachusetts. |
| Study Abroad |
Not entered |
| Study Pattern |
Lectures 20
Tutorials 0
Timetabled Laboratories 0
Non-timetabled assessed assignments 40
Private Study/Other 40
Total 100 |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Iain Murray
Tel: (0131 6)51 9078
Email: I.Murray@ed.ac.uk |
Course secretary | Ms Katey Lee
Tel: (0131 6)50 2701
Email: Katey.Lee@ed.ac.uk |
|
© Copyright 2013 The University of Edinburgh - 13 January 2014 4:27 am
|
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