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Range-based techniques for discovering optimality and analyzing scaling relationships in neuromechanical systems

Bradly J. Alicea¹

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1. Michigan State University

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Document Type:

Manuscript

Date:

Received 02 February 2009 16:57 UTC; Posted 03 February 2009

Subjects:

Neuroscience, Bioinformatics

Tags:

• morphology
• neuromuscular systems
• computational techniques
• neuromechanics

Abstract:

In this paper, a method for decoupling the neuromuscular function of a set of limbs from the role morphology plays in regulating the performance of an activity is introduced. This method is based on two previous methods: the rescaled range analysis specific to time series data, and the use of scaling laws. A review of the literature suggests that limb geometry can either facilitate or constrain performance as measured experimentally. Whether limb geometry is facilitatory or acts as a constraint depends on the size differential between arm morphology and the underlying muscle. “Changes in size and shape” are theoretically extrapolations of morphological geometry to other members of a population or species, to other species, or to technological manipulations of an individual via prosthetic devices. Three datasets are analyzed using the range-based method and a Monte-Carlo simulation, and are used to test the various ways of executing this analysis. It was found that when performance is kept stable but limb size and shape is scaled by a factor of .25, the greatest gain in performance results. It was also found that introducing force-based perturbations results in ‘shifts’ in the body geometry/performance relationship. While results such as this could be interpreted as a statistical artifact, the non-linear rise within a measurement class and linear decrease between measurement classes suggests an effect of scale in the optimality of this relationship. Overall, range-based techniques allow for the simulation and modeling of myriad changes in phenotype that result from biological and technological manipulation.

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License:

This document is licensed to the public under the Creative Commons Attribution 3.0 License

How to cite this document:

Alicea, Bradly. Range-based techniques for discovering optimality and analyzing scaling relationships in neuromechanical systems. Available from Nature Precedings <http://hdl.handle.net/10101/npre.2009.2845.1> (2009)

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Other versions of this document in Nature Precedings

Version number	Document title	Date
v2	Posted 18 June 2009

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