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In Vivo Wireless Optogenetic Control of Skilled Motor Behavior
Published on: November 22, 2021
Redundant information encoding in primary motor cortex during natural and prosthetic motor control
Kelvin So1, Karunesh Ganguly, Jessica Jimenez
1Department of Electrical Engineering and Computer Sciences, University of California, 754 Sutardja Dai Hall, Berkeley, CA 94720-1770, USA.
Journal of Computational Neuroscience
|November 2, 2011
Summary
Redundant neural encoding in the motor cortex (M1) supports reliable movement control. This redundancy is highest in brain areas most involved in controlling natural or brain-machine interface (BMI) movements.
Area of Science:
- Neuroscience
- Motor Control
- Information Theory
Background:
- Redundant information encoding is crucial for reliable information processing in distributed systems.
- Understanding redundancy in the motor cortex is key to optimizing brain-machine interfaces (BMIs).
Purpose of the Study:
- To quantify the redundancy of movement-related information in the macaque primary motor cortex (M1).
- To compare information redundancy during natural arm movement (manual control, MC) and brain-machine interface (BMI) control.
Main Methods:
- Applied information theory to analyze neural ensemble activity in macaque M1 during MC and BC.
- Monkeys performed a delay center-out reaching task controlling a cursor via arm movement or BMI.
Main Results:
- During MC, contralateral M1 neurons showed higher and more redundant target direction information than ipsilateral M1.
- During BC, M1 neurons directly used by the BMI ('direct' neurons) had the highest redundancy and information.
- Retraining the BMI with ipsilateral M1 neurons revealed higher redundancy and information in those neurons compared to contralateral M1.
Conclusions:
- Motor control ensembles with the highest association to movement exhibit the greatest redundancy and information encoding.
- Redundancy plays a significant role in both natural and prosthetic motor control proficiency.
- Findings suggest optimizing BMI control by leveraging ensembles with high inherent information redundancy.
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