A Neuromorphic Architecture for Scalable Event-Based Control
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866910027577556992 |
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| author | Huo, Yongkang Forni, Fulvio Sepulchre, Rodolphe |
| author_facet | Huo, Yongkang Forni, Fulvio Sepulchre, Rodolphe |
| contents | This paper introduces the ``rebound Winner-Take-All (RWTA)" motif as the basic element of a scalable neuromorphic control architecture. From the cellular level to the system level, the resulting architecture combines the reliability of discrete computation and the tunability of continuous regulation: it inherits the discrete computation capabilities of winner-take-all state machines and the continuous tuning capabilities of excitable biophysical circuits. The proposed event-based framework addresses continuous rhythmic generation and discrete decision-making in a unified physical modeling language. We illustrate the versatility, robustness, and modularity of the architecture through the nervous system design of a snake robot. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11924 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Neuromorphic Architecture for Scalable Event-Based Control Huo, Yongkang Forni, Fulvio Sepulchre, Rodolphe Artificial Intelligence This paper introduces the ``rebound Winner-Take-All (RWTA)" motif as the basic element of a scalable neuromorphic control architecture. From the cellular level to the system level, the resulting architecture combines the reliability of discrete computation and the tunability of continuous regulation: it inherits the discrete computation capabilities of winner-take-all state machines and the continuous tuning capabilities of excitable biophysical circuits. The proposed event-based framework addresses continuous rhythmic generation and discrete decision-making in a unified physical modeling language. We illustrate the versatility, robustness, and modularity of the architecture through the nervous system design of a snake robot. |
| title | A Neuromorphic Architecture for Scalable Event-Based Control |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2511.11924 |