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bioRxiv · 10.1101/2023.11.30.569459

Decoding multi-limb movements from low temporal resolution calcium imaging using deep learning

Park, S.·Lipton, M.·Dadarlat, M.
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Abstract

Two-photon imaging has been a critical tool for dissecting brain circuits and understanding brain function. However, relating slow two-photon calcium imaging data to fast behaviors has been challenging due to relatively low imaging sampling rates, thus limiting potential applications to neural prostheses. Here, we show that a recurrent encoder-decoder network with an output length longer than the input length can accurately decode limb trajectories of a running mouse from two-photon calcium imaging data. The encoder-decoder model could accurately decode information about all four limbs (contralateral and ipsilateral front and hind limbs) from calcium imaging data recorded in a single cortical hemisphere. Furthermore, neurons that were important for decoding were found to be well-tuned to both ipsilateral and contralateral limb movements, showing that artificial neural networks can be used to understand the function of the brain by identifying sub-networks of neurons that correlate with behaviors of interest.

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Park, S., Lipton, M., Dadarlat, M.. 2023-12-03. Decoding multi-limb movements from low temporal resolution calcium imaging using deep learning. https://doi.org/10.1101/2023.11.30.569459

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