Literature DB >> 23132431

Determining all parameters necessary to build Hill-type muscle models from experiments on single muscles.

Marcus Blümel1, Scott L Hooper, Christoph Guschlbauerc, William E White, Ansgar Büschges.   

Abstract

Characterizing muscle requires measuring such properties as force-length, force-activation, and force-velocity curves. These characterizations require large numbers of data points because both what type of function (e.g., linear, exponential, hyperbolic) best represents each property, and the values of the parameters in the relevant equations, need to be determined. Only a few properties are therefore generally measured in experiments on any one muscle, and complete characterizations are obtained by averaging data across a large number of muscles. Such averaging approaches can work well for muscles that are similar across individuals. However, considerable evidence indicates that large inter-individual variation exists, at least for some muscles. This variation poses difficulties for across-animal averaging approaches. Methods to fully describe all muscle's characteristics in experiments on individual muscles would therefore be useful. Prior work in stick insect extensor muscle has identified what functions describe each of this muscle's properties and shown that these equations apply across animals. Characterizing these muscles on an individual-by-individual basis therefore requires determining only the values of the parameters in these equations, not equation form. We present here techniques that allow determining all these parameter values in experiments on single muscles. This technique will allow us to compare parameter variation across individuals and to model muscles individually. Similar experiments can likely be performed on single muscles in other systems. This approach may thus provide a widely applicable method for characterizing and modeling muscles from single experiments.

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Year:  2012        PMID: 23132431      PMCID: PMC3505888          DOI: 10.1007/s00422-012-0531-5

Source DB:  PubMed          Journal:  Biol Cybern        ISSN: 0340-1200            Impact factor:   2.086


  17 in total

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Authors:  B R JEWELL; D R WILKIE
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3.  Slow temporal filtering may largely explain the transformation of stick insect (Carausius morosus) extensor motor neuron activity into muscle movement.

Authors:  Scott L Hooper; Christoph Guschlbauer; Géraldine von Uckermann; Ansgar Büschges
Journal:  J Neurophysiol       Date:  2007-07-11       Impact factor: 2.714

4.  Using individual-muscle specific instead of across-muscle mean data halves muscle simulation error.

Authors:  Marcus Blümel; Christoph Guschlbauer; Scott L Hooper; Ansgar Büschges
Journal:  Biol Cybern       Date:  2012-11-07       Impact factor: 2.086

5.  Double-hyperbolic force-velocity relation in frog muscle fibres.

Authors:  K A Edman
Journal:  J Physiol       Date:  1988-10       Impact factor: 5.182

6.  Natural neural output that produces highly variable locomotory movements.

Authors:  Scott L Hooper; Christoph Guschlbauer; Géraldine von Uckermann; Ansgar Büschges
Journal:  J Neurophysiol       Date:  2006-06-14       Impact factor: 2.714

Review 7.  Thixotropy in skeletal muscle and in muscle spindles: a review.

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9.  Hill-type muscle model parameters determined from experiments on single muscles show large animal-to-animal variation.

Authors:  Marcus Blümel; Christoph Guschlbauer; Silvia Daun-Gruhn; Scott L Hooper; Ansgar Büschges
Journal:  Biol Cybern       Date:  2012-11-07       Impact factor: 2.086

10.  Lobster (Panulirus interruptus) pyloric muscles express the motor patterns of three neural networks, only one of which innervates the muscles.

Authors:  Jeff B Thuma; Lee G Morris; Adam L Weaver; Scott L Hooper
Journal:  J Neurosci       Date:  2003-10-01       Impact factor: 6.167

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  9 in total

1.  Neuromodulation Can Be Simple: Myoinhibitory Peptide, Contained in Dedicated Regulatory Pathways, Is the Only Neurally-Mediated Peptide Modulator of Stick Insect Leg Muscle.

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Journal:  J Neurosci       Date:  2021-02-02       Impact factor: 6.167

2.  Fiber-type distribution in insect leg muscles parallels similarities and differences in the functional role of insect walking legs.

Authors:  Elzbieta Godlewska-Hammel; Ansgar Büschges; Matthias Gruhn
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3.  Decentralized control of insect walking: A simple neural network explains a wide range of behavioral and neurophysiological results.

Authors:  Malte Schilling; Holk Cruse
Journal:  PLoS Comput Biol       Date:  2020-04-27       Impact factor: 4.475

4.  Hill-type muscle model parameters determined from experiments on single muscles show large animal-to-animal variation.

Authors:  Marcus Blümel; Christoph Guschlbauer; Silvia Daun-Gruhn; Scott L Hooper; Ansgar Büschges
Journal:  Biol Cybern       Date:  2012-11-07       Impact factor: 2.086

5.  Control for multifunctionality: bioinspired control based on feeding in Aplysia californica.

Authors:  Victoria A Webster-Wood; Jeffrey P Gill; Peter J Thomas; Hillel J Chiel
Journal:  Biol Cybern       Date:  2020-12-10       Impact factor: 2.086

6.  Exhaustion of Skeletal Muscle Fibers Within Seconds: Incorporating Phosphate Kinetics Into a Hill-Type Model.

Authors:  Robert Rockenfeller; Michael Günther; Norman Stutzig; Daniel F B Haeufle; Tobias Siebert; Syn Schmitt; Kay Leichsenring; Markus Böl; Thomas Götz
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7.  Characterizing Motor Control of Mastication With Soft Actor-Critic.

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Journal:  Front Hum Neurosci       Date:  2020-05-26       Impact factor: 3.169

8.  A neuro-mechanical model of a single leg joint highlighting the basic physiological role of fast and slow muscle fibres of an insect muscle system.

Authors:  Tibor Istvan Toth; Joachim Schmidt; Ansgar Büschges; Silvia Daun-Gruhn
Journal:  PLoS One       Date:  2013-11-11       Impact factor: 3.240

9.  A Neuro-Musculo-Skeletal Model for Insects With Data-driven Optimization.

Authors:  Shihui Guo; Juncong Lin; Toni Wöhrl; Minghong Liao
Journal:  Sci Rep       Date:  2018-02-01       Impact factor: 4.379

  9 in total

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