title: Novelty based feedback regulation in artificial neural networks.
abstract: In this paper we present a theoretical framework for novelty based feedback regulation in artificial neural networks. Novelty is assessed on the basis of monitoring the coherence of network dynamics. The result of novelty detection is dynamically coupled to parameters that control the dynamics of the recognition process. The paper presents a new measure of novelty detection--the strength of the local field--and presents new simulation results concerning novelty detection. It also integrates previously published models and simulation results into a general dynamical model of feedback regulation.
authors.affiliation: Department of Psychology, University of Warsaw, Poland.
authors.forename: Agnieszka
authors.initials: A
authors.lastname: Rychwalska
authors.forename: Piotr
authors.initials: P
authors.lastname: Jabłoński
authors.forename: Michał
authors.initials: M
authors.lastname: Zochowski
authors.forename: Andrzej
authors.initials: A
authors.lastname: Nowak
mesh_terms: D000465:Algorithms; D005246:Feedback; D008959:Models, Neurological; D009010:Monte Carlo Method; D016571:Neural Networks, Computer