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Related Experiment Video

Updated: Jun 8, 2025

Author Spotlight: Comparative Imaging of Neural Activity in Awake and Freely Moving States
06:25

Author Spotlight: Comparative Imaging of Neural Activity in Awake and Freely Moving States

Published on: January 19, 2024

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Decoding multi-limb movements from two-photon calcium imaging of neuronal activity using deep learning.

Seungbin Park1, Megan Lipton1, Maria C Dadarlat1

  • 1Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47906, United States of America.

Journal of Neural Engineering
|November 7, 2024
PubMed
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Deep learning decodes multi-limb movements from slow optical brain recordings. This advance in brain-machine interfaces (BMIs) uses a novel recurrent neural network to interpret neural activity for controlling prosthetic limbs.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Brain-machine interfaces (BMIs) aim to restore function after neural injury.
  • Decoding movement intention from neural activity is crucial for BMIs.
  • Two-photon (2p) calcium imaging offers high-resolution neural recordings but faces challenges with slow sampling rates for decoding fast movements.

Purpose of the Study:

  • To apply deep learning to decode multi-limb movements from 2p calcium imaging data.
  • To overcome the challenge of relating slow optical imaging data to fast behaviors.
  • To establish groundwork for controlling multiple limbs using optical BMIs.

Main Methods:

  • Developed a recurrent encoder-decoder network (LSTM-encdec) with an output longer than the input.
Keywords:
brain–machine interfacedeep learningmulti-limbneural decodingoptical brain–machine interfacesensorimotortwo-photon calcium imaging

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  • Applied the LSTM-encdec model to analyze 2p calcium imaging data from sensorimotor areas of running mice.
  • Utilized interpretability measures to validate decoding accuracy.

    • Main Results:

    • The LSTM-encdec model accurately decoded information about all four limbs (contralateral and ipsilateral front and hind limbs).
    • Decoding was achieved from calcium imaging data recorded in a single cortical hemisphere.
    • The approach demonstrated the feasibility of decoding complex, multi-limb movements.

    Conclusions:

    • Deep learning, specifically the LSTM-encdec network, can effectively decode multi-limb movements from slow 2p calcium imaging data.
    • This method enhances the utility of optical imaging for brain-machine interfaces.
    • The findings pave the way for developing next-generation optical BMIs capable of controlling multiple limbs.