School of Computing

Module details

CO836 Cognitive Neural Networks (15 credits)

Syllabus

  • Introduction to cognitive neural networks.
    Neural networks will be placed into a historical perspective related to symbolic approaches and in the context of the artificial intelligence hypothesis. Students will familiarise themselves with the Leabra environment.
  • The individual neuron.
    The idea of the components of a neuron as a 'detector' will be developed. Neural networks will be explained in terms of the biology of the brain at a cellular electro-transmission level. The neurobiology will be abstracted into an initial neural network framework, i.e. a set of mathematical equations. Single neuron simulations.
  • Networks of Neurons.
    A general framework will be provided for neural network architectures both at an abstract level and in terms of networks in the cortex. Unidirectional (feedforward) and bi-directional (recurrent) interactions will be explained together with inhibitory mechanisms.
  • Model Learning.
    A simple Hebbian model of learning will be outlined, pertaining to neurobiology and neural networks. Other models of unsupervised learning will be introduced.
  • Task Learning.
    Error-driven task learning will be outlined; the delta rule and back propagation will be presented. A discussion of the biological implausibility of back propagation networks will follow. Motivated by this implausibility, the generalised recirculation algorithm will be introduced and its mathematical formulation and properties discussed.

Note

This web page provides advance information about a module due to run in the coming academic year. We believe the details are accurate at the time of writing but they may be subject to change.

School of Computing, University of Kent, Canterbury, Kent, CT2 7NF

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Last Updated: 08/04/2011 15:43