Mapping Hebbian Learning Rules to Coupling Resistances for Oscillatory Neural Networks - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Journal Articles Frontiers in Neuroscience Year : 2021

Mapping Hebbian Learning Rules to Coupling Resistances for Oscillatory Neural Networks

Aida Todri-Sanial

Abstract

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
Origin : Files produced by the author(s)

Dates and versions

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

Identifiers

Cite

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⟩
141 View
51 Download

Altmetric

Share

Gmail Facebook X LinkedIn More