title: Symbol manipulation and rule learning in spiking neuronal networks.
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.
authors.affiliation: Department of Informatics, University of Sussex, Falmer, Brighton, BN1 9RH, London, UK. ctf20@sussex.ac.uk
authors.forename: Chrisantha
authors.initials: C
authors.lastname: Fernando
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D005075:Biological Evolution; D006801:Humans; D007802:Language; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009473:Neuronal Plasticity / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D013997:Time Factors