In this module you learn what is meant by neural networks and how to explain the mathematical equations that underlie them. You also familiarise yourself with cognitive 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. The module also introduces artificial neural networks from the machine learning perspective. You will study the existing machine learning implementations of neural networks, and you will also engage in implementation of algorithms and procedures relevant to neural networks.
Private Study: 111
Contact Hours: 39
Total Hours: 150
Method of assessment
Main assessment methods
Two equally weighted practical assessments (individual; 12 hours; 20% total)
Examination (2 hours; 80%)
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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.
Bengio, Yoshua, Ian Goodfellow, and Aaron Courville. Deep learning. MIT press, 2017.
Sejnowski, Terrence J. The deep learning revolution. MIT press, 2018.
See the library reading list for this module (Canterbury)
See the library reading list for this module (Medway)
On successfully completing the Level 6 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)
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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