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Non-linear stimulus-response behavior of the human stance control system is predicted by optimization of a system with sensory and motor noise.

van der Kooij H, Peterka RJ - J Comput Neurosci (2010)

Bottom Line: Different combinations of internal sensory and/or motor noise sources were added to the model to identify the properties of noise sources that were able to account for the experimental remnant sway characteristics.Robust findings were that remnant sway characteristics were best predicted by models that included both sensory and motor noise, the graviceptive noise magnitude was about ten times larger than the proprioceptive noise, and noise sources with signal-dependent properties provided better explanations of remnant sway.Overall results indicate that humans dynamically weight sensory system contributions to stance control and tune their corrective responses to minimize the energetic effects of sensory noise and external stimuli.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomechanical Engineering, University of Twente, 7500 AE, Enschede, The Netherlands. H.vanderKooij@utwente.nl

ABSTRACT
We developed a theory of human stance control that predicted (1) how subjects re-weight their utilization of proprioceptive and graviceptive orientation information in experiments where eyes closed stance was perturbed by surface-tilt stimuli with different amplitudes, (2) the experimentally observed increase in body sway variability (i.e. the "remnant" body sway that could not be attributed to the stimulus) with increasing surface-tilt amplitude, (3) neural controller feedback gains that determine the amount of corrective torque generated in relation to sensory cues signaling body orientation, and (4) the magnitude and structure of spontaneous body sway. Responses to surface-tilt perturbations with different amplitudes were interpreted using a feedback control model to determine control parameters and changes in these parameters with stimulus amplitude. Different combinations of internal sensory and/or motor noise sources were added to the model to identify the properties of noise sources that were able to account for the experimental remnant sway characteristics. Various behavioral criteria were investigated to determine if optimization of these criteria could predict the identified model parameters and amplitude-dependent parameter changes. Robust findings were that remnant sway characteristics were best predicted by models that included both sensory and motor noise, the graviceptive noise magnitude was about ten times larger than the proprioceptive noise, and noise sources with signal-dependent properties provided better explanations of remnant sway. Overall results indicate that humans dynamically weight sensory system contributions to stance control and tune their corrective responses to minimize the energetic effects of sensory noise and external stimuli.

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Schematic depiction of the stance control system and factors potentially influencing its control. Considering the sensory integration component of the system in isolation (system in the dashed box), Bayesian estimation theory shows that optimal sensory weights can be determined that provide a maximum likelihood estimate of a physical variable if the variances of the different sensory signals are known. However, unlike simpler systems that involve only sensory integration, the feedback structure of the stance control systems causes the variability of a particular sensory signal to be influenced by intrinsic noise in all sensory systems and by motor noise, external perturbations, and the dynamic characteristics of the overall system which in turn are related to the combined influence of the sensory-integration process, the sensory-to-motor transformation, neuro-muscular dynamics, and biomechanics
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Fig1: Schematic depiction of the stance control system and factors potentially influencing its control. Considering the sensory integration component of the system in isolation (system in the dashed box), Bayesian estimation theory shows that optimal sensory weights can be determined that provide a maximum likelihood estimate of a physical variable if the variances of the different sensory signals are known. However, unlike simpler systems that involve only sensory integration, the feedback structure of the stance control systems causes the variability of a particular sensory signal to be influenced by intrinsic noise in all sensory systems and by motor noise, external perturbations, and the dynamic characteristics of the overall system which in turn are related to the combined influence of the sensory-integration process, the sensory-to-motor transformation, neuro-muscular dynamics, and biomechanics

Mentions: The control system for human bipedal upright stance involves the generation of an appropriately calibrated corrective torque based on body-sway motion detected primarily by vestibular, visual, and proprioceptive sensory systems (Horak and Macpherson 1996). Because body motions are small in this task, one expects that the signal-to-noise ratios of sensory signals are poor. Based on previous studies of sensory integration (Ernst and Banks 2002), one might hypothesize that the nervous system generates corrective torque based on an optimal estimate of body orientation derived from a weighted combination of the noisy sensory cues (Fig. 1, left side Sensory Integration component). From Bayesian estimation theory, optimal sensory weights can be determined that provide a sensory representation that is a maximum likelihood estimate of a physical variable, S, if the variances of the different sensory signals are known. An appropriately weighted combination of the noisy sensory signals and will provide an optimal estimate, , of the physical variable with lower variance than either of the individual sensory signals. For the Sensory Integration component shown in Fig. 1 and with the assumptions that the sensory noise sources are independent, Gaussian, and the Bayesian prior is uniform, then the optimal sensory weights can be calculated from the variances of the sensory signals.Fig. 1


Non-linear stimulus-response behavior of the human stance control system is predicted by optimization of a system with sensory and motor noise.

van der Kooij H, Peterka RJ - J Comput Neurosci (2010)

Schematic depiction of the stance control system and factors potentially influencing its control. Considering the sensory integration component of the system in isolation (system in the dashed box), Bayesian estimation theory shows that optimal sensory weights can be determined that provide a maximum likelihood estimate of a physical variable if the variances of the different sensory signals are known. However, unlike simpler systems that involve only sensory integration, the feedback structure of the stance control systems causes the variability of a particular sensory signal to be influenced by intrinsic noise in all sensory systems and by motor noise, external perturbations, and the dynamic characteristics of the overall system which in turn are related to the combined influence of the sensory-integration process, the sensory-to-motor transformation, neuro-muscular dynamics, and biomechanics
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3108015&req=5

Fig1: Schematic depiction of the stance control system and factors potentially influencing its control. Considering the sensory integration component of the system in isolation (system in the dashed box), Bayesian estimation theory shows that optimal sensory weights can be determined that provide a maximum likelihood estimate of a physical variable if the variances of the different sensory signals are known. However, unlike simpler systems that involve only sensory integration, the feedback structure of the stance control systems causes the variability of a particular sensory signal to be influenced by intrinsic noise in all sensory systems and by motor noise, external perturbations, and the dynamic characteristics of the overall system which in turn are related to the combined influence of the sensory-integration process, the sensory-to-motor transformation, neuro-muscular dynamics, and biomechanics
Mentions: The control system for human bipedal upright stance involves the generation of an appropriately calibrated corrective torque based on body-sway motion detected primarily by vestibular, visual, and proprioceptive sensory systems (Horak and Macpherson 1996). Because body motions are small in this task, one expects that the signal-to-noise ratios of sensory signals are poor. Based on previous studies of sensory integration (Ernst and Banks 2002), one might hypothesize that the nervous system generates corrective torque based on an optimal estimate of body orientation derived from a weighted combination of the noisy sensory cues (Fig. 1, left side Sensory Integration component). From Bayesian estimation theory, optimal sensory weights can be determined that provide a sensory representation that is a maximum likelihood estimate of a physical variable, S, if the variances of the different sensory signals are known. An appropriately weighted combination of the noisy sensory signals and will provide an optimal estimate, , of the physical variable with lower variance than either of the individual sensory signals. For the Sensory Integration component shown in Fig. 1 and with the assumptions that the sensory noise sources are independent, Gaussian, and the Bayesian prior is uniform, then the optimal sensory weights can be calculated from the variances of the sensory signals.Fig. 1

Bottom Line: Different combinations of internal sensory and/or motor noise sources were added to the model to identify the properties of noise sources that were able to account for the experimental remnant sway characteristics.Robust findings were that remnant sway characteristics were best predicted by models that included both sensory and motor noise, the graviceptive noise magnitude was about ten times larger than the proprioceptive noise, and noise sources with signal-dependent properties provided better explanations of remnant sway.Overall results indicate that humans dynamically weight sensory system contributions to stance control and tune their corrective responses to minimize the energetic effects of sensory noise and external stimuli.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomechanical Engineering, University of Twente, 7500 AE, Enschede, The Netherlands. H.vanderKooij@utwente.nl

ABSTRACT
We developed a theory of human stance control that predicted (1) how subjects re-weight their utilization of proprioceptive and graviceptive orientation information in experiments where eyes closed stance was perturbed by surface-tilt stimuli with different amplitudes, (2) the experimentally observed increase in body sway variability (i.e. the "remnant" body sway that could not be attributed to the stimulus) with increasing surface-tilt amplitude, (3) neural controller feedback gains that determine the amount of corrective torque generated in relation to sensory cues signaling body orientation, and (4) the magnitude and structure of spontaneous body sway. Responses to surface-tilt perturbations with different amplitudes were interpreted using a feedback control model to determine control parameters and changes in these parameters with stimulus amplitude. Different combinations of internal sensory and/or motor noise sources were added to the model to identify the properties of noise sources that were able to account for the experimental remnant sway characteristics. Various behavioral criteria were investigated to determine if optimization of these criteria could predict the identified model parameters and amplitude-dependent parameter changes. Robust findings were that remnant sway characteristics were best predicted by models that included both sensory and motor noise, the graviceptive noise magnitude was about ten times larger than the proprioceptive noise, and noise sources with signal-dependent properties provided better explanations of remnant sway. Overall results indicate that humans dynamically weight sensory system contributions to stance control and tune their corrective responses to minimize the energetic effects of sensory noise and external stimuli.

Show MeSH
Related in: MedlinePlus