OverviewThis module aims to give students a thorough introduction in the use and theory of cognitive neural networks. It has a theoretical component, taught in weekly 1-hour lectures. Next to that, students will have to acquire hands-on knowledge of network models in the practicals (surgeries). For psychology students, the course ends with a workshop seminar, in which each student will present on the application of neural nets to higher cognitive phenomena.
This course is a collaboration between the Schools of Computing and Psychology, under the umbrella of the Kent Centre for Cognitive Neuroscience and Cognitive Systems (CNCS).
This module appears in:
Weekly 1-hour lectures and practical classes and a workshop seminar
Only available to students registered for the MSc in Cognitive Psychology/Neuropsychology.
Method of assessment
Exercise sheets (15%); seminar presentation and write-up (25%), written examination (60%)
O'Reilly, R.C. & Munakata, Y. (2000). Computational Explorations in Cognitive Neuroscience, Understanding the Mind by Simulating the Brain. MIT Press.
Ellis, R. & Humphreys, G. (1999). Connectionist Psychology, A Text with Readings. Psychology Press.
Rumelhart, D.E., McClelland, J.L. and the PDP Research Group (1986). Parallel Distributed Processing, Volumes 1 and 2. MIT Press.
Bechtel, W. & Abrahamson, A. (2002). Connectionism and the Mind, Parallel Processing Dynamics and Evolution of Networks. Blackwell.
Haykin, S. (1999). Neural Networks, A Comprehensive Foundation. Prentice Hall.
Bishop, C.M. (1995). Neural Networks for Pattern Recognition. Oxford University Press.
To be able to describe and understand what is meant by cognitive neural networks and their applications. To be able to distinguish the major types of networks and to relate each to brain function and cognitive behaviour
To be able to select the appropriate neural network paradigm for modelling a particular aspect of cognition 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
To understand and to be able to explain the mathematical equations that underlie neural networks, both the equations that define activation transfer and those that define learning
To gain experience in analysing 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 modelling
To simulate neural networks using state of the art simulation technology and apply these networks to the modelling of human cognition. In particular, to select from the canon of learning algorithms those which are appropriate for a particular domain
To understand and be able to 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 cognitive systems not directly studied in the course in a similar fashion.