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Will the neural blackboard architecture scale up to semantics?

Published online by Cambridge University Press:  15 March 2006

Michael G. Dyer
Affiliation:
University of California at Los Angeles, Computer Science Department, UCLA, Los Angeles CA 90095 dyer@cs.ucla.edu http://www.cs.ucla.edu/~dyer/

Abstract

The neural blackboard architecture is a localist structured connectionist model that employs a novel connection matrix to implement dynamic bindings without requiring propagation of temporal synchrony. Here I note the apparent need for many distinct matrices and the effect this might have for scale-up to semantic processing. I also comment on the authors' initial foray into the symbol grounding problem.

Type
Open Peer Commentary
Copyright
© 2006 Cambridge University Press

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