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Neural Computing and Applications
Published in: Neural Computing and Applications 17/2020

09-06-2018 | S.I. : IWINAC 2015

AMSOM: artificial metaplasticity in SOM neural networks—application to MIT-BIH arrhythmias database

Authors: Santiago Torres-Alegre, Juan Fombellida, Juan Antonio Piñuela-Izquierdo, Diego Andina

Published in: Neural Computing and Applications | Issue 17/2020

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Abstract

Artificial metaplasticity is the machine learning algorithm inspired in the biological metaplasticity of neural synapses. Metaplasticity stands for plasticity of plasticity, and as long as plasticity is related to memory, metaplasticity is related to learning. Implemented in supervised learning assuming input patterns distribution or a related function, it has proved to be very efficient in performance and in training convergence for multidisciplinary applications. Now, for the first time, this kind of artificial metaplasticity is implemented in an unsupervised neural network, achieving also excellent results that are presented in this paper. To compare results, a modified self-organization map is applied to the classification of MIT-BIH cardiac arrhythmias database.
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Metadata
Title
AMSOM: artificial metaplasticity in SOM neural networks—application to MIT-BIH arrhythmias database
Authors
Santiago Torres-Alegre
Juan Fombellida
Juan Antonio Piñuela-Izquierdo
Diego Andina
Publication date
09-06-2018
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 17/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3576-0

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