Building a realistic neuronal model that simulates multi‐joint arm and hand movements in 3D space

Alstermark, B.; Lan, Ning; Pettersson, L.‐G.

doi:10.2976/1.2803419

Cited by 8 publications

(8 citation statements)

References 30 publications

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“…VM 4.0 includes both the old and the new recruitment schemes, making it suitable for analysis of neural control of movements (Alstermark et al 2007, Chan and Moran 2006, Lan et al 2005, and for design of prosthetics and FES systems to restore lost or impaired motor functions. VM 4.0 is available for downloading from http://ami.usc.edu/projects/ ami/projects/bion/musculoskeletal/virtual muscle.html.…”

Section: Introductionmentioning

confidence: 99%

Computationally efficient models of neuromuscular recruitment and mechanics

et al. 2008

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We have improved the stability and computational efficiency of a physiologically realistic, virtual muscle (VM 3.*) model (Cheng et al 2000 J. Neurosci. Methods 101 117-30) by a simpler structure of lumped fiber types and a novel recruitment algorithm. In the new version (VM 4.0), the mathematical equations are reformulated into state-space representation and structured into a CMEX S-function in SIMULINK. A continuous recruitment scheme approximates the discrete recruitment of slow and fast motor units under physiological conditions. This makes it possible to predict force output during smooth recruitment and derecruitment without having to simulate explicitly a large number of independently recruited units. We removed the intermediate state variable, effective length (Leff), which had been introduced to model the delayed length dependency of the activation-frequency relationship, but which had little effect and could introduce instability under physiological conditions of use. Both of these changes greatly reduce the number of state variables with little loss of accuracy compared to the original VM. The performance of VM 4.0 was validated by comparison with VM 3.1.5 for both single-muscle force production and a multi-joint task. The improved VM 4.0 model is more suitable for the analysis of neural control of movements and for design of prosthetic systems to restore lost or impaired motor functions. VM 4.0 is available via the internet and includes options to use the original VM model, which remains useful for detailed simulations of single motor unit behavior.

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Section: Introductionmentioning

confidence: 99%

Computationally efficient models of neuromuscular recruitment and mechanics

et al. 2008

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“…An intriguing possibility is that propriospinal pathways coordinate motor activity across multiple spinal segments to stabilize body balance, and inactivation of these pathways leads to an unstable posture. Propriospinal pathway are critical to relay sensory and supraspinal feedback across segments (Alstermark et al, 1987(Alstermark et al, , 2007Brockett et al, 2013;Ruder et al, 2016), making it appealing to speculate that these neurons sense limb stability, and following integration of mechanoreceptive and proprioceptive feedback, direct the ipsilateral limb to initiate/end the swing phase. Consistently, in mice and insects it has been shown that the posterior limb is guided forward by the position of the anterior ipsilateral limb via proprioceptive feedback (Brunn and Dean, 1994;Mayer and Akay, 2021;Theunissen et al, 2014).…”

Section: Circuits For Limb Coordination and Postural Stabilitymentioning

confidence: 99%

A spinal synergy of excitatory and inhibitory neurons coordinates ipsilateral body movements

Hayashi

Gullo

Senturk

et al. 2023

Preprint

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Innate and goal-directed movements require a high-degree of trunk and appendicular muscle coordination to preserve body stability while ensuring the correct execution of the motor action. The spinal neural circuits underlying motor execution and postural stability are finely modulated by propriospinal, sensory and descending feedback, yet how distinct spinal neuron populations cooperate to control body stability and limb coordination remains unclear. Here, we identified a spinal microcircuit composed of V2 lineage-derived excitatory (V2a) and inhibitory (V2b) neurons that together coordinate ipsilateral body movements during locomotion. Inactivation of the entire V2 neuron lineage does not impair intralimb coordination but destabilizes body balance and ipsilateral limb coupling, causing mice to adopt a compensatory festinating gait and be unable to execute skilled locomotor tasks. Taken together our data suggest that during locomotion the excitatory V2a and inhibitory V2b neurons act antagonistically to control intralimb coordination, and synergistically to coordinate forelimb and hindlimb movements. Thus, we suggest a new circuit architecture, by which neurons with distinct neurotransmitter identities employ a dual-mode of operation, exerting either synergistic or opposing functions to control different facets of the same motor behavior.

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“…Simulations must be compared to observable states of the system to validate the hypothesis advanced (Cordo et al, 2002 ; Stein et al, 2004 ; Lan and He ), or to propose new testable hypotheses (Bullock, 1993 ). Building a multi-scale model of the sensorimotor system from muscles, proprioceptors to skeletal joints, spinal regulating centers, and central control circuits exemplifies part of this endeavor (Cheng et al, 2000 ; Lan et al, 2005 ; Mileusnic et al, 2006 ; Alstermark et al, 2007 ; Song et al, 2008a , b ; Hao et al, 2013 ; He et al, 2013 ). The review article by Loeb and Tsianos outlined the necessary elements and challenges in this approach, which is the first step toward integrating a vast body of experimental data into a general mathematical framework for simulation.…”

Section: Editorialmentioning

confidence: 99%

“…Li et al used a corticospinal, virtual-arm model to investigate the central coordination of alpha and gamma controls to muscles and their muscle spindles for movement generation. Simulation results indicated that simple patterns of alpha and gamma drives are sufficient to control a range of movements, and that propriospinal neurons (Alstermark et al, 2007 ; Hao et al, 2013 ) may play an essential role in pre-motor processing of descending commands for movements.…”

Section: Editorialmentioning

confidence: 99%