Symbol manipulation and rule learning in spiking neuronal networks

Chrisantha Fernando (1)

1 Department of Informatics

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 behaviours 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 behaviours. Previously proposed neuronal implementations of symbolic representations are compared with this new proposal.

Mots clés: Spiking neural network; Neuronal replicator hypothesis; Learning classifier sysems; Spiketime-dependent plasticity

Domaines: Sciences du Vivant [q-bio]

Chrisantha Fernando. Symbol manipulation and rule learning in spiking neuronal networks. Journal of Theoretical Biology, 2011, 275 (1), pp.29. ⟨10.1016/j.jtbi.2011.01.009⟩. ⟨hal-00676101⟩