A Winner-Takes-All Mechanism for Event Generation

Fuente: arXiv
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Hauptverfasser: Huo, Yongkang, Forni, Fuvio, Sepulchre, Rodolphe
Format: Preprint
Veröffentlicht: 2025
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author Huo, Yongkang
Forni, Fuvio
Sepulchre, Rodolphe
author_facet Huo, Yongkang
Forni, Fuvio
Sepulchre, Rodolphe
contents We present a novel framework for central pattern generator design that leverages the intrinsic rebound excitability of neurons in combination with winner-takes-all computation. Our approach unifies decision-making and rhythmic pattern generation within a simple yet powerful network architecture that employs all-to-all inhibitory connections enhanced by designable excitatory interactions. This design offers significant advantages regarding ease of implementation, adaptability, and robustness. We demonstrate its efficacy through a ring oscillator model, which exhibits adaptive phase and frequency modulation, making the framework particularly promising for applications in neuromorphic systems and robotics.
format Preprint
id arxiv_https___arxiv_org_abs_2504_11374
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle A Winner-Takes-All Mechanism for Event Generation
Huo, Yongkang
Forni, Fuvio
Sepulchre, Rodolphe
Systems and Control
Artificial Intelligence
We present a novel framework for central pattern generator design that leverages the intrinsic rebound excitability of neurons in combination with winner-takes-all computation. Our approach unifies decision-making and rhythmic pattern generation within a simple yet powerful network architecture that employs all-to-all inhibitory connections enhanced by designable excitatory interactions. This design offers significant advantages regarding ease of implementation, adaptability, and robustness. We demonstrate its efficacy through a ring oscillator model, which exhibits adaptive phase and frequency modulation, making the framework particularly promising for applications in neuromorphic systems and robotics.
title A Winner-Takes-All Mechanism for Event Generation
topic Systems and Control
Artificial Intelligence
url https://arxiv.org/abs/2504.11374