Optimal reaching subject to computational and physical constraints reveals structure of the sensorimotor control system

Optimal feedback control provides an abstract framework describing the architecture of the sensorimotor system without prescribing implementation details such as what coordinate system to use, how feedback is incorporated, or how to accommodate changing task complexity. We investigate how such detai...

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Published inProceedings of the National Academy of Sciences - PNAS Vol. 121; no. 14; p. e2319313121
Main Authors Greene, Patrick, Bastian, Amy J., Schieber, Marc H., Sarma, Sridevi V.
Format Journal Article
LanguageEnglish
Published United States National Academy of Sciences 02.04.2024
Subjects
Barriers
Biological Sciences
Constraint modelling
Control systems
Coordinates
Feedback
Feedback control
Feedback, Sensory - physiology
Learning
Muscles
Neurons
Objective function
Optimization
Placement
Sensorimotor system
Task complexity
sensorimotor system
computational neuroscience
motor control
optimal feedback
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Summary:Optimal feedback control provides an abstract framework describing the architecture of the sensorimotor system without prescribing implementation details such as what coordinate system to use, how feedback is incorporated, or how to accommodate changing task complexity. We investigate how such details are determined by computational and physical constraints by creating a model of the upper limb sensorimotor system in which all connection weights between neurons, feedback, and muscles are unknown. By optimizing these parameters with respect to an objective function, we find that the model exhibits a preference for an intrinsic (joint angle) coordinate representation of inputs and feedback and learns to calculate a weighted feedforward and feedback error. We further show that complex reaches around obstacles can be achieved by augmenting our model with a path-planner based on via points. The path-planner revealed “avoidance” neurons that encode directions to reach around obstacles and “placement” neurons that make fine-tuned adjustments to via point placement. Our results demonstrate the surprising capability of computationally constrained systems and highlight interesting characteristics of the sensorimotor system.
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Edited by Peter Strick, University of Pittsburgh Brain Institute, Pittsburgh, PA; received November 3, 2023; accepted February 13, 2024
2M.H.S. and S.V.S. contributed equally to this work.
ISSN:0027-8424
1091-6490
1091-6490
DOI:10.1073/pnas.2319313121