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      A kinematic model of stick‐insect walking

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          Abstract

          Animal, and insect walking (locomotion) in particular, have attracted much attention from scientists over many years up to now. The investigations included behavioral, electrophysiological experiments, as well as modeling studies. Despite the large amount of material collected, there are left many unanswered questions as to how walking and related activities are generated, maintained, and controlled. It is obvious that for them to take place, precise coordination within muscle groups of one leg and between the legs is required: intra‐ and interleg coordination. The nature, the details, and the interactions of these coordination mechanisms are not entirely clear. To help uncover them, we made use of modeling techniques, and succeeded in developing a six‐leg model of stick‐insect walking. Our main goal was to prove that the same model can mimic a variety of walking‐related behavioral modes, as well as the most common coordination patterns of walking just by changing the values of a few input or internal variables. As a result, the model can reproduce the basic coordination patterns of walking: tetrapod and tripod and the transition between them. It can also mimic stop and restart, change from forward‐to‐backward walking and back. Finally, it can exhibit so‐called search movements of the front legs both while walking or standing still. The mechanisms of the model that enable it to produce the aforementioned behavioral modes can hint at and prove helpful in uncovering further details of the biological mechanisms underlying walking.

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          Most cited references 54

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          Dynamic sensorimotor interactions in locomotion.

          Locomotion results from intricate dynamic interactions between a central program and feedback mechanisms. The central program relies fundamentally on a genetically determined spinal circuitry (central pattern generator) capable of generating the basic locomotor pattern and on various descending pathways that can trigger, stop, and steer locomotion. The feedback originates from muscles and skin afferents as well as from special senses (vision, audition, vestibular) and dynamically adapts the locomotor pattern to the requirements of the environment. The dynamic interactions are ensured by modulating transmission in locomotor pathways in a state- and phase-dependent manner. For instance, proprioceptive inputs from extensors can, during stance, adjust the timing and amplitude of muscle activities of the limbs to the speed of locomotion but be silenced during the opposite phase of the cycle. Similarly, skin afferents participate predominantly in the correction of limb and foot placement during stance on uneven terrain, but skin stimuli can evoke different types of responses depending on when they occur within the step cycle. Similarly, stimulation of descending pathways may affect the locomotor pattern in only certain phases of the step cycle. Section ii reviews dynamic sensorimotor interactions mainly through spinal pathways. Section iii describes how similar sensory inputs from the spinal or supraspinal levels can modify locomotion through descending pathways. The sensorimotor interactions occur obviously at several levels of the nervous system. Section iv summarizes presynaptic, interneuronal, and motoneuronal mechanisms that are common at these various levels. Together these mechanisms contribute to the continuous dynamic adjustment of sensorimotor interactions, ensuring that the central program and feedback mechanisms are congruous during locomotion.
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            The Dynamics of Legged Locomotion: Models, Analyses, and Challenges

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              Insect walking.

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                Author and article information

                Contributors
                silvia.daun@uni-koeln.de
                Journal
                Physiol Rep
                Physiol Rep
                10.1002/(ISSN)2051-817X
                PHY2
                physreports
                Physiological Reports
                John Wiley and Sons Inc. (Hoboken )
                2051-817X
                29 April 2019
                April 2019
                : 7
                : 8 ( doiID: 10.1002/phy2.2019.7.issue-8 )
                Affiliations
                [ 1 ] Department of Biology Faculty of Mathematical and Natural Sciences Heisenberg Research Group of Computational Neuroscience – Modeling Neuronal Network Function University of Cologne Koeln Germany
                [ 2 ] Jülich Research Center Institute of Neuroscience and Medicine INM‐3 Koeln Germany
                Author notes
                [* ] Correspondence

                Silvia Daun, Department of Animal Physiology, Institute of Zoology, University of Cologne Zuülpicher Strasse 47b, D‐50674 Cologne, Germany.

                Tel: +49 221 4703829

                Fax: +49 221 4704889

                E‐mail: silvia.daun@ 123456uni-koeln.de

                Article
                PHY214080
                10.14814/phy2.14080
                6487367
                31033245
                © 2019 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                Page count
                Figures: 15, Tables: 0, Pages: 26, Words: 17783
                Product
                Funding
                Funded by: DFG
                Award ID: DA1953/4‐2
                Categories
                Cognitive and Behavioural Neuroscience
                Motor Control
                Original Research
                Original Research
                Custom metadata
                2.0
                phy214080
                April 2019
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.6.2.1 mode:remove_FC converted:29.04.2019

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