Modeling Retinal Ganglion Cells with Neural Differential Equations

Kacper Dobek; Daniel Jankowski; Krzysztof Krawiec

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arXiv · 2511.18014

Modeling Retinal Ganglion Cells with Neural Differential Equations

Kacper Dobek·Daniel Jankowski·Krzysztof Krawiec

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Abstract

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.

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Kacper Dobek, Daniel Jankowski, Krzysztof Krawiec. 2025-11-22. Modeling Retinal Ganglion Cells with Neural Differential Equations. https://arxiv.org/abs/2511.18014

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