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Neural Geometry from Mixed Sensorimotor Selectivity for Predictive Sensorimotor Control

View ORCID ProfileYiheng Zhang, Yun Chen, View ORCID ProfileTianwei Wang, View ORCID ProfileHe Cui
doi: https://doi.org/10.1101/2023.04.06.535795
Yiheng Zhang
1Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
2Chinese Institute for Brain Research, Beijing, 102206, China
3University of Chinese Academy of Sciences, Beijing 100049, China
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  • ORCID record for Yiheng Zhang
Yun Chen
1Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
2Chinese Institute for Brain Research, Beijing, 102206, China
3University of Chinese Academy of Sciences, Beijing 100049, China
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Tianwei Wang
1Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
2Chinese Institute for Brain Research, Beijing, 102206, China
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He Cui
1Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
2Chinese Institute for Brain Research, Beijing, 102206, China
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Abstract

Although recent studies suggest that activity in the motor cortex, in addition to generating motor outputs, receives substantial information regarding sensory inputs, it is still unclear how sensory context adjusts the motor commands. Here, we recorded population neural activity in the motor cortex via microelectrode arrays while monkeys performed flexible manual interceptions of moving targets. During this task, which requires predictive sensorimotor control, the activity of most neurons in the motor cortex encoding upcoming movements, was influenced by ongoing target motion. Single-trial neural states at the movement onset formed staggered orbital geometries, suggesting that target motion modulates peri-movement activity in an orthogonal manner. This neural geometry was further evaluated with a representational model and recurrent neural networks (RNNs) with task-specific input-output mapping. We propose that the sensorimotor dynamics can be derived from neuronal mixed sensorimotor selectivity and dynamic interaction between modulations.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • We have revised the RNN model, clarified the mixed tuning of single neuron; updated Discussion and Supplemental file.

  • https://data.mendeley.com/datasets/8gngr6tphf/2

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted January 05, 2025.
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Neural Geometry from Mixed Sensorimotor Selectivity for Predictive Sensorimotor Control
Yiheng Zhang, Yun Chen, Tianwei Wang, He Cui
bioRxiv 2023.04.06.535795; doi: https://doi.org/10.1101/2023.04.06.535795
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Neural Geometry from Mixed Sensorimotor Selectivity for Predictive Sensorimotor Control
Yiheng Zhang, Yun Chen, Tianwei Wang, He Cui
bioRxiv 2023.04.06.535795; doi: https://doi.org/10.1101/2023.04.06.535795

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