Mapping Hebbian Learning Rules to Coupling Resistances for Oscillatory Neural Networks - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier
Article Dans Une Revue Frontiers in Neuroscience Année : 2021

Mapping Hebbian Learning Rules to Coupling Resistances for Oscillatory Neural Networks

Corentin Delacour
Aida Todri-Sanial

Résumé

Oscillatory Neural Network (ONN) is an emerging neuromorphic architecture with oscillators representing neurons and information encoded in oscillator's phase relations. In an ONN, oscillators are coupled with electrical elements to define the network's weights and achieve massive parallel computation. As the weights preserve the network functionality, mapping weights to coupling elements plays a crucial role in ONN performance. In this work, we investigate relaxation oscillators based on VO2 material, and we propose a methodology to map Hebbian coefficients to ONN coupling resistances, allowing a large-scale ONN design. We develop an analytical framework to map weight coefficients into coupling resistor values to analyze ONN architecture performance. We report on an ONN with 60 fully-connected oscillators that perform pattern recognition as a Hopfield Neural Network.
Fichier principal
Vignette du fichier
fnins-15-694549.pdf (3.86 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

lirmm-03197299 , version 1 (11-11-2021)

Identifiants

Citer

Corentin Delacour, Aida Todri-Sanial. Mapping Hebbian Learning Rules to Coupling Resistances for Oscillatory Neural Networks. Frontiers in Neuroscience, 2021, 15, pp.#694549. ⟨10.3389/fnins.2021.694549⟩. ⟨lirmm-03197299⟩
161 Consultations
85 Téléchargements

Altmetric

Partager

More