Connectionists: Symbol manipulation and rule learning in spiking neuronal networks
Chrisantha Fernando
ctf20 at sussex.ac.uk
Thu Feb 17 01:32:12 EST 2011
FYI...
Journal of Theoretical Biology
Volume 275, Issue 1, 21 April 2011, Pages 29-41
Symbol manipulation and rule learning in spiking neuronal networks
Chrisantha Fernandoa, b, c, ,
a Department of Informatics, University of Sussex, Falmer, Brighton, BN1 9RH, London, UK
b MRC National Institute for Medical Research, The Ridgeway, Mill Hill, London, UK
c Collegium Budapest, Institute for Advanced Study, Szentháromság u. 2, H-1014 Budapest, Hungary
Received 19 August 2010; revised 9 January 2011; accepted 10 January 2011. Available online 13 January 2011.
Abstract
It has been claimed that the productivity, systematicity and compositionality of human language and thought necessitate the existence of a physical symbol system (PSS) in the brain. Recent discoveries about temporal coding suggest a novel type of neuronal implementation of a physical symbol system. Furthermore, learning classifier systems provide a plausible algorithmic basis by which symbol re-write rules could be trained to undertake behaviors exhibiting systematicity and compositionality, using a kind of natural selection of re-write rules in the brain, We show how the core operation of a learning classifier system, namely, the replication with variation of symbol re-write rules, can be implemented using spike-time dependent plasticity based supervised learning. As a whole, the aim of this paper is to integrate an algorithmic and an implementation level description of a neuronal symbol system capable of sustaining systematic and compositional behaviors. Previously proposed neuronal implementations of symbolic representations are compared with this new proposal.
Available here...
http://www.cogs.susx.ac.uk/users/ctf20/dphil_2005/publications.htm
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