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
