Implementation of input correlation learning with an optoelectronic dendritic unit
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Published:2023-03-15
Issue:
Volume:11
Page:
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ISSN:2296-424X
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Container-title:Frontiers in Physics
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language:
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Short-container-title:Front. Phys.
Author:
Ortín Silvia,Soriano Miguel C.,Tetzlaff Christian,Wörgötter Florentin,Fischer Ingo,Mirasso Claudio R.,Argyris Apostolos
Abstract
The implementation of machine learning concepts using optoelectronic and photonic components is rapidly advancing. Here, we use the recently introduced notion of optical dendritic structures, which aspires to transfer neurobiological principles to photonics computation. In real neurons, plasticity—the modification of the connectivity between neurons due to their activity—plays a fundamental role in learning. In the current work, we investigate theoretically and experimentally an artificial dendritic structure that implements a modified Hebbian learning model, called input correlation (ICO) learning. The presented optical fiber-based dendritic structure employs the summation of the different optical intensities propagating along the optical dendritic branches and uses Gigahertz-bandwidth modulation via semiconductor optical amplifiers to apply the necessary plasticity rules. In its full deployment, this optoelectronic ICO learning analog can be an efficient hardware platform for ultra-fast control.
Funder
Horizon 2020 Framework Programme
Publisher
Frontiers Media SA
Subject
Physical and Theoretical Chemistry,General Physics and Astronomy,Mathematical Physics,Materials Science (miscellaneous),Biophysics