Undergraduate Course: Neural Computation (INFR11162)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Year 4 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | **This course replaces Neural Computation (INFR11008)**
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.) |
| Course description |
*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
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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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. |
Information for Visiting Students
| Pre-requisites | Experience in programming or simulation systems desirable. Fair amount of mathematics (first order differential equations, eigenvectors, descriptive statistics). No background in Neuroscience is necessary. Visiting students are required to have comparable background to that assumed by the course prerequisites listed in the Degree Regulations & Programmes of Study. If in doubt, consult the course lecturer. |
| High Demand Course? |
Yes |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student 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.
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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. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | NC |
Contacts
| Course organiser | Dr Peggy Series
Tel: (0131 6)50 3088
Email: |
Course secretary | Miss Clara Fraser
Tel: (0131 6)51 4164
Email: |
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