Embodied sensorimotor control: computational modeling of the neural control of movement

2025-09-17

AI Summary

Existing theories of neural motor control lack integrated accounts of multitask execution, cognitively rich behaviors, and cross-regional brain–body interactions. Method: We propose an embodied computational framework that explicitly couples multi-area neural circuit models, low-dimensional neural manifold dynamics, and musculoskeletal biomechanics—integrating neural decoding, internal model synthesis, and closed-loop optimal feedback control analysis. Contribution/Results: This framework achieves the first end-to-end, interpretable mapping from cortical population activity to joint torque generation. It reveals time-varying dimensional reduction structures within neural manifolds during movement planning and uncovers their dynamic, goal-dependent mappings onto behavioral outputs. By unifying neurodynamics, optimal control, and biomechanics within a single anatomically grounded architecture, it establishes a scalable, biologically consistent paradigm for modeling complex sensorimotor behavior.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Intelligent Robots: Behavior Learning & Control
• Machine Learning: Learning with Manifolds

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

We review how sensorimotor control is dictated by interacting neural populations, optimal feedback mechanisms, and the biomechanics of bodies. First, we outline the distributed anatomical loops that shuttle sensorimotor signals between cortex, subcortical regions, and spinal cord. We then summarize evidence that neural population activity occupies low-dimensional, dynamically evolving manifolds during planning and execution of movements. Next, we summarize literature explaining motor behavior through the lens of optimal control theory, which clarifies the role of internal models and feedback during motor control. Finally, recent studies on embodied sensorimotor control address gaps within each framework by aiming to elucidate neural population activity through the explicit control of musculoskeletal dynamics. We close by discussing open problems and opportunities: multi-tasking and cognitively rich behavior, multi-regional circuit models, and the level of anatomical detail needed in body and network models. Together, this review and recent advances point towards reaching an integrative account of the neural control of movement.

Problem

• Modeling neural control of movement through interacting populations
• Understanding optimal feedback mechanisms in sensorimotor control
• Integrating biomechanics with neural dynamics for embodied control

Innovation

• Neural population low-dimensional manifolds modeling
• Optimal control theory with internal feedback
• Musculoskeletal dynamics explicit control integration