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Proceedings Paper

Weakly connected oscillatory networks for dynamic pattern recognition
Author(s): Marco Gilli; Michele Bonnin; Fernando Corinto
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Paper Abstract

Many studies in neuroscience have shown that nonlinear dynamic networks represent a bio-inspired models for information and image processing. Recent studies on the thalamo-cortical system have shown that weakly connected oscillatory networks have the capability of modelling the architecture of a neurocomputer. In particular they have associative properties and can be exploited for dynamic pattern recognition. In this manuscript the global dynamic behavior of weakly connected cellular networks of oscillators are investigated. It is assumed that each cell admits of a Lur'e description. In case of weak coupling the main dynamic features of the network are revealed by the phase deviation equation (i.e. the equation that describes the phase deviation due to the weak coupling). Firstly a very accurate analytic expression of the phase deviation equation is derived via the joint application of the describing function technique and of Malkin's Theorem. Then it is shown that the total number of periodic limit cycles with their stability properties can be estimated through the analysis of the phase deviation equation.

Paper Details

Date Published: 29 June 2005
PDF: 12 pages
Proc. SPIE 5839, Bioengineered and Bioinspired Systems II, (29 June 2005); doi: 10.1117/12.608498
Show Author Affiliations
Marco Gilli, Politecnico di Torino (Italy)
Michele Bonnin, Politecnico di Torino (Italy)
Fernando Corinto, Politecnico di Torino (Italy)


Published in SPIE Proceedings Vol. 5839:
Bioengineered and Bioinspired Systems II
Ricardo A. Carmona; Gustavo Linan-Cembrano, Editor(s)

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