Introduction to Neural Computing & Applications - COM00007H

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  • Department: Computer Science
  • Module co-ordinator: Dr. Simon O'Keefe
  • Credit value: 20 credits
  • Credit level: H
  • Academic year of delivery: 2017-18
    • See module specification for other years: 2016-17

Module occurrences

Occurrence Teaching cycle
A Autumn Term 2017-18 to Summer Term 2017-18

Module aims

Module aim is to introduce the student to the main computational models of neurons, from the simplest sum-and-threshold units through to detailed compartmental models, and understand the relevance and applicability of the various models to computational problems. The student will learn about training paradigms for artificial neuron networks and how this relates to learning in biological networks. The aim is to equip the student to use artificial neural networks appropriately to solve a wide range of problems, and to provide some of the fundamental ideas from neuroscience that will allow the student to undertake further study of neural networks.

Module learning outcomes

On completion of this module, students will:

  • Understand neuron models (artificial and biologically motivated)
  • Be able to create neural networks with appropriate architectures
  • Understand and be able to apply learning and training algorithms
  • Apply neural networks to real problems
  • Understand the analysis of performance of neural networks

Academic and graduate skills
On completion of this module, students will have:

  • Developed their written communication skills
  • Developed their analysis and problem solving skills
  • Developed their academic reading skills

Module content

Prerequisite knowledge

Mathematical background in the following will be assumed or introduced:

  • Matrices and vectors
  • Linear algebra
  • Solution of systems of equations
  • Eigenvalues and eigenvectors
  • Partial differentiation
  • Differentiation of scalar functions of vectors and matrices
  • Probability density functions

In addition, knowledge of passive circuit elements and RC circuits will help in understanding material in two lectures.


Task Length % of module mark
Introduction to Neural Computing & Applications (INCA) Report
N/A 100

Special assessment rules



Task Length % of module mark
Introduction to Neural Computing & Applications (INCA) Report
N/A 100

Module feedback

Feedback to students will be available via:

  • Verbal feedback on practical sessions.
  • Model or outline solutions to exercises, usually 2 weeks after related lab sessions, for self-evaluation.
  • Feedback on practical assessment within 4 term weeks of submission.

Indicative reading

** Haykin, S., Neural Networks:a comprehensive foundation, 3rd ed, Pearson, 2009

* Bishop, C., Neural Networks for Pattern Recognition, OUP, 1995

* Sterrat et al., Principles of Computational Modelling in Neuroscience, OUP, 2011

++ Callan, The Essence of Neural Networks, Prentice Hall, 1999

++ Beale and Jackson, Neural Computing: an introduction, Institute of Physics, 1990

++ Anderson, J.A., An Introduction to Neural Networks, MIT Press, 1995

The information on this page is indicative of the module that is currently on offer. The University is constantly exploring ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.