Design of Oscillatory Neural Networks Using Machine-Learned Templates

MOAYED Mitra; Csaba György: Design of Oscillatory Neural Networks Using Machine-Learned Templates.
ELECTRONICS (SWITZ), 15 (13). ISSN 2079-9292 (2026)

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Szerző azonosítók:

Név	ORCID	MTMT szerző azonosító
MOAYED Mitra		10087710
Csaba György		10057150

Absztrakt (kivonat):

Oscillatory neural networks (ONNs) provide a neuromorphic computing framework that exploits the phase dynamics of coupled oscillators for parallel and energy-efficient pattern recognition. In this study, we design a single-layer, fully connected ONN to classify handwritten digits from the MNIST dataset. Input images were downsampled to 6 × 6 binary patterns, which were optimized using a genetic algorithm to evolve effective templates, as experiments with higher-resolution inputs showed only marginal accuracy improvements at significantly increased computational and energy costs. Coupling weights were determined using Hebbian learning, and the network dynamics were simulated using the Kuramoto model to encode information via phase relationships. To the best of our knowledge, this is the first work to apply genetic algorithm optimization to design the templates used by an ONN and to combine evolutionary template generation with Hebbian-based ONN training for image classification. The results show that the ONN achieves 75–76% accuracy in the full 10-class MNIST task, with outputs exhibiting stable sinusoidal behavior and resilience to moderate noise. These findings highlight the potential of ONNs as a practical, low-power alternative to conventional deep learning models, particularly for real-time edge-level applications where energy efficiency and robustness are critical.

Folyóirat címe:

ELECTRONICS (SWITZ)

Megjelenés éve:

2026

Kötet:

Szám:

ISSN:

2079-9292

Intézmény:

Pázmány Péter Katolikus Egyetem

Kar:

Információs Technológiai és Bionikai Kar (2013.07.-)