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

• 成果类型: Article
• 署名作者: Greene, Patrick; Bastian, Amy J.; Schieber, Marc H.; Sarma, Sridevi, V
• 署名单位: Johns Hopkins University; Kennedy Krieger Institute; Johns Hopkins University; University of Rochester; Johns Hopkins University
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-13278
• DOI: 10.1073/pnas.2319313121
• 发表日期: 2024-04-04
• 关键词: primary motor cortex; optimal feedback-control; arm movements; forelimb muscles; premotor cortex; direction; REPRESENTATIONS; specification; INFORMATION

摘要

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.