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      Future Portrait of the Athletic Brain: Mechanistic Understanding of Human Sport Performance Via Animal Neurophysiology of Motor Behavior

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          Abstract

          Sport performances are often showcases of skilled motor control. Efforts to understand the neural processes subserving such movements may teach us about general principles of behavior, similarly to how studies on neurological patients have guided early work in cognitive neuroscience. While investigations on non-human animal models offer valuable information on the neural dynamics of skilled motor control that is still difficult to obtain from humans, sport sciences have paid relatively little attention to these mechanisms. Similarly, knowledge emerging from the study of sport performance could inspire innovative experiments in animal neurophysiology, but the latter has been only partially applied. Here, we advocate that fostering interactions between these two seemingly distant fields, i.e., animal neurophysiology and sport sciences, may lead to mutual benefits. For instance, recording and manipulating the activity from neurons of behaving animals offer a unique viewpoint on the computations for motor control, with potentially untapped relevance for motor skills development in athletes. To stimulate such transdisciplinary dialog, in the present article, we also discuss steps for the reverse translation of sport sciences findings to animal models and the evaluation of comparability between animal models of a given sport and athletes. In the final section of the article, we envision that some approaches developed for animal neurophysiology could translate to sport sciences anytime soon (e.g., advanced tracking methods) or in the future (e.g., novel brain stimulation techniques) and could be used to monitor and manipulate motor skills, with implications for human performance extending well beyond sport.

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          DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

          Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.
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            The relation of strength of stimulus to rapidity of habit-formation

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              The BDNF val66met Polymorphism Affects Activity-Dependent Secretion of BDNF and Human Memory and Hippocampal Function

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                Author and article information

                Contributors
                Journal
                Front Syst Neurosci
                Front Syst Neurosci
                Front. Syst. Neurosci.
                Frontiers in Systems Neuroscience
                Frontiers Media S.A.
                1662-5137
                17 November 2020
                2020
                : 14
                : 596200
                Affiliations
                Physiological Sciences Section, Department of Experimental and Clinical Medicine, University of Florence , Florence, Italy
                Author notes

                Edited by: Alessandro E. P. Villa, Neuro-heuristic Research Group (NHRG), Switzerland

                Reviewed by: José M. Delgado-García, Universidad Pablo de Olavide, Spain; Jeffery Allen Boychuk, The University of Texas Health Science Center at San Antonio, United States

                *Correspondence: Diego Minciacchi, diego.minciacchi@ 123456unifi.it
                Article
                10.3389/fnsys.2020.596200
                7705174
                19c45fd6-af6d-4e0a-8e6b-99f5a8030255
                Copyright © 2020 Quarta, Cohen, Bravi and Minciacchi.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 18 August 2020
                : 19 October 2020
                Page count
                Figures: 2, Tables: 0, Equations: 0, References: 152, Pages: 12, Words: 0
                Categories
                Neuroscience
                Perspective

                Neurosciences
                information processing,motor control,neural networks,animal models,sport performance
                Neurosciences
                information processing, motor control, neural networks, animal models, sport performance

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