Learning Interpretable Control Inputs and Dynamics Underlying Animal Locomotion
Published in The Twelfth International Conference on Learning Representations, 2024
This paper presents an optimal control theory approach to modelling animal locomotion, using sparse control signals and recurrent neural networks to learn the dynamics underlying larval Zebrafish and C. elegans movement patterns.
Recommended citation: Soares Mullen*, T., Schimel*, M., Hennequin, G., Machens, C., Orger, M., Jouary, A.. (2024). "Learning Interpretable Control Inputs and Dynamics Underlying Animal Locomotion." The Twelfth International Conference on Learning Representations.
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