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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.02803 |
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| _version_ | 1866917975918903296 |
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| author | Hendrickson, Aaron J. Haefner, David P. |
| author_facet | Hendrickson, Aaron J. Haefner, David P. |
| contents | A novel continuous-time framework is proposed for modeling neuromorphic image sensors in the form of an initial canonical representation with analytical tractability. Exact simulation algorithms are developed in parallel with closed-form expressions that characterize the model's dynamics. This framework enables the generation of synthetic event streams in genuine continuous-time, which combined with the analytical results, reveal the underlying mechanisms driving the oscillatory behavior of event data presented in the literature. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02803 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Discretization: A Continuous-Time Framework for Event Generation in Neuromorphic Pixels Hendrickson, Aaron J. Haefner, David P. Applications A novel continuous-time framework is proposed for modeling neuromorphic image sensors in the form of an initial canonical representation with analytical tractability. Exact simulation algorithms are developed in parallel with closed-form expressions that characterize the model's dynamics. This framework enables the generation of synthetic event streams in genuine continuous-time, which combined with the analytical results, reveal the underlying mechanisms driving the oscillatory behavior of event data presented in the literature. |
| title | Beyond Discretization: A Continuous-Time Framework for Event Generation in Neuromorphic Pixels |
| topic | Applications |
| url | https://arxiv.org/abs/2504.02803 |