How Frequency Injection Locking Can Train Oscillatory Neural Networks to Compute in Phase

Aida Todri-Sanial (Corresponding author), Stefania Carapezzi, Corentin Delacour, Madeleine Abernot, Thierry Gil, Elisabetta Corti, Siegfried F. Karg, Juan Nüñez, Manuel Jiménèz, María J. Avedillo, Bernabé Linares-Barranco

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

29 Citaten (Scopus)

Samenvatting

Brain-inspired computing employs devices and architectures that emulate biological functions for more adaptive and energy-efficient systems. Oscillatory neural networks (ONNs) are an alternative approach in emulating biological functions of the human brain and are suitable for solving large and complex associative problems. In this work, we investigate the dynamics of coupled oscillators to implement such ONNs. By harnessing the complex dynamics of coupled oscillatory systems, we forge a novel computation model - information is encoded in the phase of oscillations. Coupled interconnected oscillators can exhibit various behaviors due to the strength of the coupling. In this article, we present a novel method based on subharmonic injection locking (SHIL) for controlling the oscillatory states of coupled oscillators that allow them to lock in frequency with distinct phase differences. Circuit-level simulation results indicate SHIL effectiveness and its applicability to large-scale oscillatory networks for pattern recognition.

Originele taal-2Engels
Pagina's (van-tot)1996-2009
Aantal pagina's14
TijdschriftIEEE Transactions on Neural Networks and Learning Systems
Volume33
Nummer van het tijdschrift5
DOI's
StatusGepubliceerd - 1 mei 2022
Extern gepubliceerdJa

Financiering

FinanciersFinanciernummer
European Union’s Horizon Europe research and innovation programme871501

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