Chaotic Dynamics in Iterated Map Neural Networks with Piecewise Linear Activation Function

dc.creatorSinha, Sitabhra
dc.date1999-03-06
dc.date.accessioned2026-07-07T02:35:40Z
dc.date.available2026-07-07T02:35:40Z
dc.descriptionThe paper examines the discrete-time dynamics of neuron models (of excitatory and inhibitory types) with piecewise linear activation functions, which are connected in a network. The properties of a pair of neurons (one excitatory and the other inhibitory) connected with each other, is studied in detail. Even such a simple system shows a rich variety of behavior, including high-period oscillations and chaos. Border-collision bifurcations and multifractal fragmentation of the phase space is also observed for a range of parameter values. Extension of the model to a larger number of neurons is suggested under certain restrictive assumptions, which makes the resultant network dynamics effectively one-dimensional. Possible applications of the network for information processing are outlined. These include using the network for auto-association, pattern classification, nonlinear function approximation and periodic sequence generation.
dc.description20 pages, using fuin.cls (included). 12 figures. To appear in Fundamenta Informaticae (1999)
dc.identifierhttps://arxiv.org/abs/chao-dyn/9903009
dc.identifierhttp://arxiv.org/abs/chao-dyn/9903009
dc.identifierFundamenta Informaticae, 37 (1999) 31-50
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/15694
dc.subjectChaotic Dynamics
dc.subjectNeurons and Cognition
dc.titleChaotic Dynamics in Iterated Map Neural Networks with Piecewise Linear Activation Function
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