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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2404.14591 |
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| _version_ | 1866913338831667200 |
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| author | Hou, Yuchen Pullela, Laya Su, Jiaxin Aluru, Sriya Sista, Shivani Lu, Xiankun Beyeler, Michael |
| author_facet | Hou, Yuchen Pullela, Laya Su, Jiaxin Aluru, Sriya Sista, Shivani Lu, Xiankun Beyeler, Michael |
| contents | Retinal implants are a promising treatment option for degenerative retinal disease. While numerous models have been developed to simulate the appearance of elicited visual percepts ("phosphenes"), these models often either focus solely on spatial characteristics or inadequately capture the complex temporal dynamics observed in clinical trials, which vary heavily across implant technologies, subjects, and stimulus conditions. Here we introduce two computational models designed to accurately predict phosphene fading and persistence under varying stimulus conditions, cross-validated on behavioral data reported by nine users of the Argus II Retinal Prosthesis System. Both models segment the time course of phosphene perception into discrete intervals, decomposing phosphene fading and persistence into either sinusoidal or exponential components. Our spectral model demonstrates state-of-the-art predictions of phosphene intensity over time (r = 0.7 across all participants). Overall, this study lays the groundwork for enhancing prosthetic vision by improving our understanding of phosphene temporal dynamics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_14591 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Predicting the Temporal Dynamics of Prosthetic Vision Hou, Yuchen Pullela, Laya Su, Jiaxin Aluru, Sriya Sista, Shivani Lu, Xiankun Beyeler, Michael Computational Engineering, Finance, and Science Retinal implants are a promising treatment option for degenerative retinal disease. While numerous models have been developed to simulate the appearance of elicited visual percepts ("phosphenes"), these models often either focus solely on spatial characteristics or inadequately capture the complex temporal dynamics observed in clinical trials, which vary heavily across implant technologies, subjects, and stimulus conditions. Here we introduce two computational models designed to accurately predict phosphene fading and persistence under varying stimulus conditions, cross-validated on behavioral data reported by nine users of the Argus II Retinal Prosthesis System. Both models segment the time course of phosphene perception into discrete intervals, decomposing phosphene fading and persistence into either sinusoidal or exponential components. Our spectral model demonstrates state-of-the-art predictions of phosphene intensity over time (r = 0.7 across all participants). Overall, this study lays the groundwork for enhancing prosthetic vision by improving our understanding of phosphene temporal dynamics. |
| title | Predicting the Temporal Dynamics of Prosthetic Vision |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2404.14591 |