Operator-Splitting Methods for Neuromorphic Circuit Simulation
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908382837866496 |
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| author | Shahhosseini, Amir Chaffey, Thomas Sepulchre, Rodolphe |
| author_facet | Shahhosseini, Amir Chaffey, Thomas Sepulchre, Rodolphe |
| contents | A novel splitting algorithm is proposed for the numerical simulation of neuromorphic circuits. The algorithm is grounded in the operator-theoretic concept of monotonicity, which bears both physical and algorithmic significance. The splitting exploits this correspondence to translate the circuit architecture into the algorithmic architecture. The paper illustrates the many advantages of the proposed operator-theoretic framework over conventional numerical integration for the simulation of multiscale hierarchical events that characterize neuromorphic behaviors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_22363 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Operator-Splitting Methods for Neuromorphic Circuit Simulation Shahhosseini, Amir Chaffey, Thomas Sepulchre, Rodolphe Systems and Control A novel splitting algorithm is proposed for the numerical simulation of neuromorphic circuits. The algorithm is grounded in the operator-theoretic concept of monotonicity, which bears both physical and algorithmic significance. The splitting exploits this correspondence to translate the circuit architecture into the algorithmic architecture. The paper illustrates the many advantages of the proposed operator-theoretic framework over conventional numerical integration for the simulation of multiscale hierarchical events that characterize neuromorphic behaviors. |
| title | Operator-Splitting Methods for Neuromorphic Circuit Simulation |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2505.22363 |