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A Minimal Model Describing Hexapedal Interlimb Coordination: The Tegotae-Based Approach

Overview of attention for article published in Frontiers in Neurorobotics, June 2017
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Title
A Minimal Model Describing Hexapedal Interlimb Coordination: The Tegotae-Based Approach
Published in
Frontiers in Neurorobotics, June 2017
DOI 10.3389/fnbot.2017.00029
Pubmed ID
Authors

Dai Owaki, Masashi Goda, Sakiko Miyazawa, Akio Ishiguro

Abstract

Insects exhibit adaptive and versatile locomotion despite their minimal neural computing. Such locomotor patterns are generated via coordination between leg movements, i.e., an interlimb coordination, which is largely controlled in a distributed manner by neural circuits located in thoracic ganglia. However, the mechanism responsible for the interlimb coordination still remains elusive. Understanding this mechanism will help us to elucidate the fundamental control principle of animals' agile locomotion and to realize robots with legs that are truly adaptive and could not be developed solely by conventional control theories. This study aims at providing a "minimal" model of the interlimb coordination mechanism underlying hexapedal locomotion, in the hope that a single control principle could satisfactorily reproduce various aspects of insect locomotion. To this end, we introduce a novel concept we named "Tegotae," a Japanese concept describing the extent to which a perceived reaction matches an expectation. By using the Tegotae-based approach, we show that a surprisingly systematic design of local sensory feedback mechanisms essential for the interlimb coordination can be realized. We also use a hexapod robot we developed to show that our mathematical model of the interlimb coordination mechanism satisfactorily reproduces various insects' gait patterns.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 36 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 22%
Student > Ph. D. Student 8 22%
Researcher 4 11%
Student > Doctoral Student 2 6%
Other 2 6%
Other 4 11%
Unknown 8 22%
Readers by discipline Count As %
Engineering 13 36%
Computer Science 5 14%
Neuroscience 4 11%
Business, Management and Accounting 2 6%
Agricultural and Biological Sciences 2 6%
Other 2 6%
Unknown 8 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 23 July 2017.
All research outputs
#13,557,147
of 22,979,862 outputs
Outputs from Frontiers in Neurorobotics
#269
of 875 outputs
Outputs of similar age
#161,467
of 317,132 outputs
Outputs of similar age from Frontiers in Neurorobotics
#7
of 14 outputs
Altmetric has tracked 22,979,862 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 875 research outputs from this source. They receive a mean Attention Score of 4.1. This one has gotten more attention than average, scoring higher than 66% 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 317,132 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 14 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 50% of its contemporaries.