Neural Comput - Noninvertibility, chaotic coding, and chaotic multiplexity of synaptically modulated neural firing.

Tópicos

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Resumo

Widely accepted neural firing and synaptic potentiation rules specify a cross-dependence of the two processes, which, evolving on different timescales, have been separated for analytic purposes, concealing essential dynamics. Here, the morphology of the firing rates process, modulated by synaptic potentiation, is shown to be described by a discrete iteration map in the form of a thresholded polynomial. Given initial synaptic weights, a firing activity is triggered by conductance. Elementary dynamic modes are defined by fixed points, cycles, and saddles of the map, building blocks of the underlying firing code. Showing parameter-dependent multiplicity of real polynomial roots, the map is proved to be noninvertible. The incidence of chaos is then implied by the parameter-dependent existence of snap-back repellers. The highly patterned geometric and statistical structures of the associated chaotic attractors suggest that these attractors are an integral part of the neural code. It further suggests the chaotic attractor as a natural mechanism for statistical encoding and temporal multiplexing of neural information. The analytic findings are supported by simulation.

Resumo Limpo

wide accept neural fire synapt potenti rule specifi crossdepend two process evolv differ timescal separ analyt purpos conceal essenti dynam morpholog fire rate process modul synapt potenti shown describ discret iter map form threshold polynomi given initi synapt weight fire activ trigger conduct elementari dynam mode defin fix point cycl saddl map build block under fire code show parameterdepend multipl real polynomi root map prove noninvert incid chao impli parameterdepend exist snapback repel high pattern geometr statist structur associ chaotic attractor suggest attractor integr part neural code suggest chaotic attractor natur mechan statist encod tempor multiplex neural inform analyt find support simul

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