Accessibility navigation


Dynamic knowledge representation in connectionist systems

Bishop, J. M., Nasuto, S. J. and de Meyer, K. (2002) Dynamic knowledge representation in connectionist systems. In: Artificial Neural Networks - ICANN'02, 28-30 Aug 2002, Madrid, Spain, pp. 308-313.

Full text not archived in this repository.

Official URL: http://dl.acm.org/citation.cfm?id=758867&CFID=3760...

Abstract/Summary

One of the most pervading concepts underlying computational models of information processing in the brain is linear input integration of rate coded uni-variate information by neurons. After a suitable learning process this results in neuronal structures that statically represent knowledge as a vector of real valued synaptic weights. Although this general framework has contributed to the many successes of connectionism, in this paper we argue that for all but the most basic of cognitive processes, a more complex, multi-variate dynamic neural coding mechanism is required - knowledge should not be spacially bound to a particular neuron or group of neurons. We conclude the paper with discussion of a simple experiment that illustrates dynamic knowledge representation in a spiking neuron connectionist system.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:18635
Additional Information:Proceedings ISBN: 3540440747

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation