http://precedings.nature.com/users/c03207e4b991dafd9a9bb5d13e39dd6b/ Documents posted by Bradly Alicea Nature Publishing Group en Nature Precedings http://precedings.nature.com/images/header_logo.gif http://precedings.nature.com http://dx.doi.org/10.1038/npre.2009.2845.2 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. http://dx.doi.org/10.1038/npre.2009.2845.2 Thu, 18 Jun 2009 08:09:20 UTC Range-based techniques for discovering optimality and analyzing scaling relationships in neuromechanical systems doi:10.1038/npre.2009.2845.2 2009-06-18 Bradly J. Alicea Nature Precedings 2009-06-18T08:09:20Z Manuscript Neuroscience Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/3301/version/1 Stochastic resonance (SR) is a concept from the physics and engineering communities that has applicability to both systems physiology and other living systems. In this paper, it will be argued that stochastic resonance plays a role in driving behavior in neuromechanical systems. The theory of stochastic resonance will be discussed, followed by a series of expected outcomes, and two tests of stochastic resonance in an experimental setting. These tests are exploratory in nature, and provide a means to parameterize systems that couple biological and mechanical components. Finally, the potential role of stochastic resonance in adaptive physiological systems will be discussed. http://precedings.nature.com/documents/3301/version/1 Tue, 02 Jun 2009 20:16:48 UTC Stochastic Resonance Can Drive Adaptive Physiological Processes hdl:10101/npre.2009.3301.1 2009-06-02 Bradly J. Alicea Nature Precedings 2009-06-02T20:16:48Z Manuscript Neuroscience http://creativecommons.org/licenses/by/3.0/