School of Computing

Module details

CO636 Cognitive Neural Networks (15 credits)

Syllabus

  • Introduction to cognitive neural networks.
    The basic motivation for 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. An overview of the general approach to be taken throughout the course will be given. The course text O'Reilly and Munakata "Computational Explorations in Cognitive Neuroscience" will be introduced.
    Practicals: Students will familiarise themselves with the Leabra environment.
  • The individual neuron.
    Developing the idea of the components of a neuron as a "detector". Neural networks will be explained in terms of the biology of the brain at a cellular electro-transmission level. This will be followed by abstracting the neurobiology into an initial neural network framework, i.e. a set of mathematical equations.
    Practicals: Students will run single neuron simulations and appraise their level of understanding. Students will work through the exercises/explorations in Chapter 2.
  • Networks of neurons.
    A general framework 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.
    Practicals: Students will work through the explorations in Chapter 3.
  • Model Learning.
    These lectures will provide the theoretical outline of a simple Hebbian model of learning, pertaining to neurobiology and neural networks. It will also introduce other models of unsupervised learning.
    Practicals: Students will work through the explorations in Chapter 4.
  • Task Learning.
    Error-driven task learning; the delta rule and back propagation will be presented. A discussion of the biological implausibility of backpropagation networks will follow.
    Practicals: Students will work through the explorations in Chapter 5.
  • Combined model, task learning and other mechanisms.
    The advantages and disadvantages of Hebbian and Error driven learning and how these different methods of learning may be combined.
    Individual explorations: Students will work through the explorations in Chapter 6.
  • The brain and implications for biologically plausible neural networks.
    A broad framework of biologically plausible neural networks and how this framework relates to brain architecture and function.
  • Perception, Vision, Object Recognition and Attention.
    From the lower level representations of vision to the higher level of object recognition. The neural networks considered will be placed within the context of the human dual route (what-where) visual system.
    Individual explorations: Explorations will be based on the ability of the "what-where" pathway to influence the network's allocation of attention to spatial locations (Chapter 8).

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: 13/01/2010 16:10