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This chapter introduces NEUROiD, an integrated co-simulation framework designed to model human movement by coupling neural circuitry with biomechanical systems. NEUROiD combines NEURON-based simulations of spinal and cortical networks with OpenSim musculoskeletal models, enabling the simulation of closed-loop motor behaviour driven by realistic neural dynamics and proprioceptive feedback. The framework supports subject-specific personalization, allowing detailed exploration of how anatomical variability and neural impairments interact to shape motor outcomes. Case studies demonstrate its versatility: spinal micro-stimulation experiments replicate segmental movement patterns; virtual tendon transfer surgeries reveal the interplay between altered musculotendon geometry and reflex modulation; simulations of spasticity using the Modified Tardieu Test reproduce velocity-dependent resistance and clasp-knife effects; and reinforcement learning agents trained within NEUROiD learn biologically plausible control strategies through interaction with the virtual body. Together, these studies underscore NEUROiD’s utility in bridging brain, body, and environment for research in motor control, rehabilitation, and intelligent neuro-prosthetic design.

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NEUROiD: Building Neuro-musculoskeletal Models of Upper and Lower Limb In-Silico by Bridging Neural Circuitry, Biomechanics, and Intelligent Control

  • 章节

  • 发表日期：

    2026年

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  • Avinash Kumar Singh,
  • Kapardi Mallampalli,
  • Yashaswini Mandayam Rangayyan,
  • Raghu Sesha Iyengar,
  • Mohan Raghavan

摘要

This chapter introduces NEUROiD, an integrated co-simulation framework designed to model human movement by coupling neural circuitry with biomechanical systems. NEUROiD combines NEURON-based simulations of spinal and cortical networks with OpenSim musculoskeletal models, enabling the simulation of closed-loop motor behaviour driven by realistic neural dynamics and proprioceptive feedback. The framework supports subject-specific personalization, allowing detailed exploration of how anatomical variability and neural impairments interact to shape motor outcomes. Case studies demonstrate its versatility: spinal micro-stimulation experiments replicate segmental movement patterns; virtual tendon transfer surgeries reveal the interplay between altered musculotendon geometry and reflex modulation; simulations of spasticity using the Modified Tardieu Test reproduce velocity-dependent resistance and clasp-knife effects; and reinforcement learning agents trained within NEUROiD learn biologically plausible control strategies through interaction with the virtual body. Together, these studies underscore NEUROiD’s utility in bridging brain, body, and environment for research in motor control, rehabilitation, and intelligent neuro-prosthetic design.