Neural Computation (P00871)

? Credit Points : 10 ? SCQF Level : 11 ? Acronym : INF-P-NC

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

? Pre-requisites : For Informatics PG students only, or by special permission of the School. Experience in programming or simulation systems desirable. Fair amount of mathematics (first order differential eqs., eigenvectors, descriptive statistics). No background in Neuroscience is necessary.

Subject Areas

Home subject area

Bioinformatics, (School of Informatics, Schedule O)

Other subject areas

Cognitive Neuroscience, (School of Informatics, Schedule O)

Computational Systems Biology, (School of Informatics, Schedule O)

Neuroinformatics, (School of Informatics, Schedule O)

Delivery Information

? Normal year taken : Postgraduate

? Delivery Period : Semester 2 (Blocks 3-4)

? Contact Teaching Time : 2 hour(s) per week for 10 weeks

First Class Information

Date	Start	End	Room	Area	Additional Information
08/01/2008	10:00	10:50	Room G.04, William Robertson Building	Central

All of the following classes

Type	Day	Start	End	Area
Lecture	Tuesday	10:00	10:50	Central
Lecture	Friday	10:00	10:50	Central

Summary of Intended Learning Outcomes

It is anticipated that students who complete the course will be able to:
-Demonstrate a basic knowledge of neuroscience and neural computation.
-Abstract neuroscience experimental data to a model and should be able to critically evaluate these models.
-Understand the major limitations in verifying the model experimentally.