Dynamic knowledge representation in connectionist systems

Bishop, Mark (J. M.); Nasuto, S. and De Meyer, K.. 2002. Dynamic knowledge representation in connectionist systems. Lecture Notes in Computer Science, 2415, pp. 308-313. ISSN 0302-9743 [Article]

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Abstract or Description

One of the most pervading concepts underlying computa-
tional 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.

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Date Deposited:

01 Dec 2015 12:17

Last Modified:

20 Jun 2017 09:39

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Yes, this version has been peer-reviewed.



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