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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/2511.18014 |
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| _version_ | 1866909917554671616 |
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| author | Dobek, Kacper Jankowski, Daniel Krawiec, Krzysztof |
| author_facet | Dobek, Kacper Jankowski, Daniel Krawiec, Krzysztof |
| contents | This work explores Liquid Time-Constant Networks (LTCs) and Closed-form Continuous-time Networks (CfCs) for modeling retinal ganglion cell activity in tiger salamanders across three datasets. Compared to a convolutional baseline and an LSTM, both architectures achieved lower MAE, faster convergence, smaller model sizes, and favorable query times, though with slightly lower Pearson correlation. Their efficiency and adaptability make them well suited for scenarios with limited data and frequent retraining, such as edge deployments in vision prosthetics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18014 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Modeling Retinal Ganglion Cells with Neural Differential Equations Dobek, Kacper Jankowski, Daniel Krawiec, Krzysztof Computer Vision and Pattern Recognition Artificial Intelligence This work explores Liquid Time-Constant Networks (LTCs) and Closed-form Continuous-time Networks (CfCs) for modeling retinal ganglion cell activity in tiger salamanders across three datasets. Compared to a convolutional baseline and an LSTM, both architectures achieved lower MAE, faster convergence, smaller model sizes, and favorable query times, though with slightly lower Pearson correlation. Their efficiency and adaptability make them well suited for scenarios with limited data and frequent retraining, such as edge deployments in vision prosthetics. |
| title | Modeling Retinal Ganglion Cells with Neural Differential Equations |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2511.18014 |