In this module you learn what is meant by neural networks and how to explain the mathematical equations that underlie them. You also build neural networks using state of the art simulation technology and apply these networks to the solution of problems. In addition, the module discusses examples of computation applied to neurobiology and cognitive psychology.
Total contact hours: 38
Private study hours: 112
Total study hours: 150
Method of assessment
13.1 Main assessment methods
20% Coursework and 80% Examination
Two Simulations assessments, 12 hours total (20%)
Examination, 2 hours (80%)
13.2 Reassessment methods
Like for like.
R.C. O'Reilly and Y. Munakata "Computational Explorations in Cognitive Neuroscience, Understanding the Mind by Simulating the Brain" A Bradford Book, MIT Press 2000
D.E. Rumelhart, J.L. McClelland and the PDP Research Group "Parallel Distributed Processing, Volume 1: Foundations" MIT Press 1986
D.E. Rumelhart, J.L. McClelland and the PDP Research Group "Parallel Distributed Processing, Volume 2: Psychological and Biological Models" MIT Press 1986
W. Bechtel and A. Abrahamson "Connectionism and the Mind, Parallel Processing Dynamics and Evolution of Networks" Blackwell Publishers 2002
S. Haykin "Neural Networks, A Comprehensive Foundation" Prentice Hall International Edition 1999
C.M. Bishop "Neural Networks for Pattern Recognition" Oxford University Press 1995
R. Ellis and G. Humphreys "Connectionist Psychology, A Text with Readings" Psyhology Press Publishers 1999
See the library reading list for this module (Canterbury)
See the library reading list for this module (Medway)
The intended subject specific learning outcomes.
On successfully completing the module students will be able to:
1 Describe what is meant by neural networks, list a number of types of network and give a brief description of each together with some examples of their (actual or
2 Select the appropriate neural network paradigm for a particular problem and be able to justify this choice based on knowledge of the properties and potential of this
paradigm. To be able to compare the general capabilities of a number of such paradigms and give an overview of their comparative strengths and weaknesses.
3 Explain the mathematical equations that underlie neural networks, both the equations that define activation transfer and those that define learning.
4 Analyse cognitive and neurobiological phenomena from the point of view of their being computational systems. To be able to take these phenomena and identify the
features which are important for computational problem solving.
5 Build neural networks using state of the art simulation technology and apply these networks to the solution of problems. In particular, to select from the canon of learning
algorithms which is appropriate for a particular problem domain.
6 Discuss examples of computation applied to neurobiology and cognitive psychology, both in the instrumental sense of the application of computers in modelling and in the
sense of using computational concepts as a way of understanding how biological and cognitive systems function. To be able to analyse related systems not directly
studied in the course in a similar fashion.
7 Discuss examples of neural networks as applied to neurobiology.
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Credit level 6. Higher level module usually taken in Stage 3 of an undergraduate degree.
- ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
- The named convenor is the convenor for the current academic session.
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