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Sensor-Motor Maps for Describing Linear Reflex Composition in Hopping

Overview of attention for article published in Frontiers in Computational Neuroscience, November 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

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Title
Sensor-Motor Maps for Describing Linear Reflex Composition in Hopping
Published in
Frontiers in Computational Neuroscience, November 2017
DOI 10.3389/fncom.2017.00108
Pubmed ID
Authors

Christian Schumacher, André Seyfarth

Abstract

In human and animal motor control several sensory organs contribute to a network of sensory pathways modulating the motion depending on the task and the phase of execution to generate daily motor tasks such as locomotion. To better understand the individual and joint contribution of reflex pathways in locomotor tasks, we developed a neuromuscular model that describes hopping movements. In this model, we consider the influence of proprioceptive length (LFB), velocity (VFB) and force feedback (FFB) pathways of a leg extensor muscle on hopping stability, performance and efficiency (metabolic effort). Therefore, we explore the space describing the blending of the monosynaptic reflex pathway gains. We call this reflex parameter space a sensor-motor map. The sensor-motor maps are used to visualize the functional contribution of sensory pathways in multisensory integration. We further evaluate the robustness of these sensor-motor maps to changes in tendon elasticity, body mass, segment length and ground compliance. The model predicted that different reflex pathway compositions selectively optimize specific hopping characteristics (e.g., performance and efficiency). Both FFB and LFB were pathways that enable hopping. FFB resulted in the largest hopping heights, LFB enhanced hopping efficiency and VFB had the ability to disable hopping. For the tested case, the topology of the sensor-motor maps as well as the location of functionally optimal compositions were invariant to changes in system designs (tendon elasticity, body mass, segment length) or environmental parameters (ground compliance). Our results indicate that different feedback pathway compositions may serve different functional roles. The topology of the sensor-motor map was predicted to be robust against changes in the mechanical system design indicating that the reflex system can use different morphological designs, which does not apply for most robotic systems (for which the control often follows a specific design). Consequently, variations in body mechanics are permitted with consistent compositions of sensory feedback pathways. Given the variability in human body morphology, such variations are highly relevant for human motor control.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 28%
Student > Master 5 13%
Student > Doctoral Student 2 5%
Researcher 2 5%
Student > Postgraduate 2 5%
Other 4 10%
Unknown 14 35%
Readers by discipline Count As %
Engineering 11 28%
Sports and Recreations 3 8%
Nursing and Health Professions 2 5%
Computer Science 2 5%
Business, Management and Accounting 1 3%
Other 3 8%
Unknown 18 45%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 October 2018.
All research outputs
#5,601,189
of 23,008,860 outputs
Outputs from Frontiers in Computational Neuroscience
#252
of 1,354 outputs
Outputs of similar age
#107,900
of 438,449 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#10
of 28 outputs
Altmetric has tracked 23,008,860 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,354 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one has done well, scoring higher than 81% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 438,449 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.