Cognitive Neural Networks - COMP8360

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2021 to 2022
Autumn Term 7 15 (7.5) Howard Bowman checkmark-circle


Neural networks will be placed into a historical perspective related to neuro-biology and in the context of the artificial intelligence hypothesis. Students will familiarise themselves with the Leabra/Emergent environment.


Contact hours

Total contact hours: 46
Private study hours: 104
Total study hours: 150

Method of assessment

Main assessment methods
30% Coursework and 70% Examination

Two simulation assessments (15% total)
Talk in workshop (15%)
Examination (70%)

Reassessment methods
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Indicative reading

O'Reilly, R.C. and Munakata, Y. (2000) Computational Explorations in Cognitive Neuroscience, Understanding the Mind by Simulating the Brain. A Bradford Book, MIT Press.
Rumelhart, D.E., McClelland J.L. and the PDP Research Group (1986) Parallel Distributed Processing, Volume 1: Foundations. MIT Press.
Rumelhart, D.E., McClelland J.L., and the PDP Research Group (1986) Parallel Distributed Processing, Volume 2: Psychological and Biological Models. MIT Press.
Bechtel, W. and Abrahamson, A. (2002) Connectionism and the Mind, Parallel Processing Dynamics and Evolution of Networks. Blackwell Publishers.
Haykin, S. (1999) Neural Networks, A Comprehensive Foundation. Prentice Hall International Edition.
Bishop, C.M. (1995) Neural Networks for Pattern Recognition. Oxford University Press.
Ellis, R. and Humphreys, G. (1999) Connectionist Psychology, A Text with Readings. Psychology Press Publishers.

See the library reading list for this module (Canterbury)

See the library reading list for this module (Medway)

Learning outcomes

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
potential) applications.
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 Simulate and understand 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.
7 To have a detailed knowledge of an advanced specialised topic in cognitive neural networks. Furthermore, the student should be able to explain the key details of one or
more of these specialised topics.
8 To have the capacity to engage with the research literature in Computational Neuroscience.

The intended generic learning outcomes.
On successfully completing the module students will be able to:
1 Group work.
2 Time management and organisation.
3 Communication skills.
4 Problem solving.
5 Analytical skills.
6 Independent study and appropriate use of resources, e.g. the library, online resources and internet sites.


  1. Credit level 7. Undergraduate or postgraduate masters level module.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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