Feasibility Theory Reconciles and Informs Alternative Approaches to Neuromuscular Control

Cohn, Brian A.; Szedlák, May; Gärtner, Bernd; Valero-Cuevas, Francisco J.

doi:10.3389/fncom.2018.00062

Cited by 23 publications

(50 citation statements)

References 82 publications

(198 reference statements)

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“…The neural circuitry for such tasks has been less studied because of poor animal models and methodological complexity. Valero-Cuevas introduced the notion of “feasible activation space” for the 7 muscles that enable the human index finger to generate 3D force vectors at the tip ( Cohn, Szedlák, Gärtner, & Valero-Cuevas, 2018 ; Francisco J Valero-Cuevas, 2016 ; Francisco J Valero-Cuevas, Zajac, & Burgar, 1998 ). Many patterns of muscle recruitment can generate a given low-force vector, but these apparently redundant solutions disappear gradually and naturally as the required forces increase.…”

Section: How Might Humans Learn To Use Muscles?mentioning

confidence: 99%

Learning to Use Muscles

Loeb

2021

Journal of Human Kinetics

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The human musculoskeletal system is highly complex mechanically. Its neural control must deal successfully with this complexity to perform the diverse, efficient, robust and usually graceful behaviors of which humans are capable. Most of those behaviors might be performed by many different subsets of its myriad possible states, so how does the nervous system decide which subset to use? One solution that has received much attention over the past 50 years would be for the nervous system to be fundamentally limited in the patterns of muscle activation that it can access, a concept known as muscle synergies or movement primitives. Another solution, based on engineering control methodology, is for the nervous system to compute the single optimal pattern of muscle activation for each task according to a cost function. This review points out why neither appears to be the solution used by humans. There is a third solution that is based on trial-and-error learning, recall and interpolation of sensorimotor programs that are good-enough rather than limited or optimal. The solution set acquired by an individual during the protracted development of motor skills starting in infancy forms the basis of motor habits, which are inherently low-dimensional. Such habits give rise to muscle usage patterns that are consistent with synergies but do not reflect fundamental limitations of the nervous system and can be shaped by training or disability. This habit-based strategy provides a robust substrate for the control of new musculoskeletal structures during evolution as well as for efficient learning, athletic training and rehabilitation therapy.

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Section: How Might Humans Learn To Use Muscles?mentioning

confidence: 99%

Learning to Use Muscles

Loeb

2021

Journal of Human Kinetics

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“…Human movements are subject to both inherent physiological noise [ 1 , 2 ] and multiple levels of redundancy [ 3 – 5 ]: i.e., the body has more mechanical degrees-of-freedom than needed to execute most movements, more muscles than needed to move a given joint, etc. Likewise, most tasks we perform exhibit equifinality [ 3 , 6 – 8 ]: i.e., we can achieve them with equal success by an infinite number of movements [ 9 – 12 ].…”

Section: Introductionmentioning

confidence: 99%

Humans use multi-objective control to regulate lateral foot placement when walking

2019

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A fundamental question in human motor neuroscience is to determine how the nervous system generates goal-directed movements despite inherent physiological noise and redundancy. Walking exhibits considerable variability and equifinality of task solutions. Existing models of bipedal walking do not yet achieve both continuous dynamic balance control and the equifinality of foot placement humans exhibit. Appropriate computational models are critical to disambiguate the numerous possibilities of how to regulate stepping movements to achieve different walking goals. Here, we extend a theoretical and computational Goal Equivalent Manifold (GEM) framework to generate predictive models, each posing a different experimentally testable hypothesis. These models regulate stepping movements to achieve any of three hypothesized goals, either alone or in combination: maintain lateral position, maintain lateral speed or “heading”, and/or maintain step width. We compared model predictions against human experimental data. Uni-objective control models demonstrated clear redundancy between stepping variables, but could not replicate human stepping dynamics. Most multi-objective control models that balanced maintaining two of the three hypothesized goals also failed to replicate human stepping dynamics. However, multi-objective models that strongly prioritized regulating step width over lateral position did successfully replicate all of the relevant step-to-step dynamics observed in humans. Independent analyses confirmed this control was consistent with linear error correction and replicated step-to-step dynamics of individual foot placements. Thus, the regulation of lateral stepping movements is inherently multi-objective and balances task-specific trade-offs between competing task goals. To determine how people walk in their environment requires understanding both walking biomechanics and how the nervous system regulates movements from step-to-step. Analogous to mechanical “templates” of locomotor biomechanics, our models serve as “control templates” for how humans regulate stepping movements from each step to the next. These control templates are symbiotic with well-established mechanical templates, providing complimentary insights into walking regulation.

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“…The sensitive region of a feasible force set to loss of a single muscle comprises all the force directions and magnitudes where the muscle is necessary to generate the maximal force [15]. However, feasible muscle activation ranges, which explicitly identify the degree of possible variation in a single muscle’s activity [40], were largely unconstrained in many cases, demonstrating the biomechanical latitude that the nervous system has when selecting muscle activation patterns for the same motor task. As such, multiple functional criteria such as stability, resistance to fatigue, or generalizability [41–43], rather than single optimality [38, 44–49], may underlie the diversity in muscle activation patterns observed across individuals with varying motor training or neurological health [50].…”

Section: Discussionmentioning

confidence: 99%

Effects of kinematic complexity and number of muscles on musculoskeletal model robustness to muscle dysfunction

2019

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The robustness of motor outputs to muscle dysfunction has been investigated using musculoskeletal modeling, but with conflicting results owing to differences in model complexity and motor tasks. Our objective was to systematically study how the number of kinematic degrees of freedom, and the number of independent muscle actuators alter the robustness of motor output to muscle dysfunction. We took a detailed musculoskeletal model of the human leg and systematically varied the model complexity to create six models with either 3 or 7 kinematic degrees of freedom and either 14, 26, or 43 muscle actuators. We tested the redundancy of each model by quantifying the reduction in sagittal plane feasible force set area when a single muscle was removed. The robustness of feasible force set area to the loss of any single muscle, i.e. general single muscle loss increased with the number of independent muscles and decreased with the number of kinematic degrees of freedom, with the robust area varying from 1% and 52% of the intact feasible force set area. The maximum sensitivity of the feasible force set to the loss of any single muscle varied from 75% to 26% of the intact feasible force set area as the number of muscles increased. Additionally, the ranges of feasible muscle activation for maximum force production were largely unconstrained in many cases, indicating ample musculoskeletal redundancy even for maximal forces. We propose that ratio of muscles to kinematic degrees of freedom can be used as a rule of thumb for estimating musculoskeletal redundancy in both simulated and real biomechanical systems.

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Feasibility Theory Reconciles and Informs Alternative Approaches to Neuromuscular Control

Cited by 23 publications

References 82 publications

Learning to Use Muscles

Learning to Use Muscles

Humans use multi-objective control to regulate lateral foot placement when walking

Effects of kinematic complexity and number of muscles on musculoskeletal model robustness to muscle dysfunction

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