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      Modeling search movements of an insect's front leg

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          Abstract

          Beside locomotion, search movements are another important type of motor activity of insects. They are very often performed by the front legs of the animals. They consist of cyclic stereotypical leg movements that can be modified by sensory signals. The details of the local organization of these movements have however not yet been studied. In this paper, we, using an appropriate variant of our existing one‐leg model, present a scheme of how these searching movements might be organized and performed on the level of local neuromuscular control networks. In the simulations with the model, we attempted to mimic the experimental results by Berg et al. (J. Exp. Biol. 216:1064–1074, 2013) in which an obstacle was put in the way of the search movements of the front leg for a very short while, and then the recovery to the usual search movements was observed and analyzed. Our simulation results suggest that the recruitment of the fast levator and depressor muscles play a crucial part in resuming the search movements after removal of the obstacle. The interplay between the levator and depressor, and the extensor and flexor local control networks can, according to the model, bring about a large variety of search movements upon removal of the obstacle. A number of these movements are comparable with those seen in the experiments.

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          Most cited references21

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          Goal-driven behavioral adaptations in gap-climbing Drosophila.

          Tasks such as reaching out toward a distant target require adaptive and goal-oriented muscle-activity patterns. The CNS likely composes such patterns from behavioral subunits. How this coordination is done is a central issue in neural motor control. Here, we present a novel paradigm, which allows us to address this question in Drosophila with neurogenetic tools. Freely walking flies are faced with a chasm in their way. Whether they initiate gap-crossing behavior at all and how vigorously they try to reach the other side of the gap depend on a visual estimate of the gap width. By interfering with various putative distance-measuring mechanisms, we found that flies chiefly use the vertical edges on the targeted side to distill the gap width from the parallax motion generated during the approach. At gaps of surmountable width, flies combine and successively improve three behavioral adaptations to maximize the front-leg reach. Each leg pair contributes in a different manner. A screen for climbing mutants yielded lines with defects in the control of climbing initiation and others with specific impairments of particular behavioral adaptations while climbing. The fact that the adaptations can be impaired separately unveils them as distinct subunits.
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            Load sensing and control of posture and locomotion.

            This article reviews recent findings on how forces are detected by sense organs of insect legs and how this information is integrated in control of posture and walking. These experiments have focused upon campaniform sensilla, receptors that detect forces as strains in the exoskeleton, and include studies of sensory discharges in freely moving animals and intracellular characterization of connectivity of afferent inputs in the central nervous system. These findings provide insights into how campaniform sensilla can contribute to the adjustment of motor outputs to changes in load. In this review we discuss (1) anatomy of the receptors and their activities in freely moving insects, (2) mechanisms by which inputs are incorporated into motor outputs and (3) the integration of sensory signals of diverse modalities. The discharges of some groups of receptors can encode body load when standing. Responses are also correlated with muscle-generated forces during specific times in walking. These activities can enhance motor outputs through reflexes and can affect the timing of motoneuron firing through inputs to pattern generating interneurons. Flexibility in the system is also provided by interactions of afferent inputs at several levels. These mechanisms can contribute to the adaptability of insect locomotion to diverse terrains and environments.
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              Stick insect locomotion in a complex environment: climbing over large gaps.

              In a complex environment, animals are challenged by various types of obstacles. This requires the controller of their walking system to be highly flexible. In this study, stick insects were presented with large gaps to cross in order to observe how locomotion can be adapted to challenging environmental situations. Different approaches were used to investigate the sequence of gap-crossing behaviour. A detailed video analysis revealed that gap-crossing behaviour resembles modified walking behaviour with additional step types. The walking sequence is interrupted by an interval of exploration, in which the insect probes the gap space with its antennae and front legs. When reaching the gap, loss of contact of an antenna with the ground does not elicit any observable reactions. In contrast, an initial front leg step into the gap that often follows antennal 'non-contact' evokes slowing down of stance velocity. An ablation experiment showed that the far edge of the gap is detected by tactile antennal stimulation rather than by vision. Initial contact of an antenna or front leg with the far edge of the gap represents a 'point of no return', after which gap crossing is always successfully completed. Finally, flow chart diagrams of the gap-crossing sequence were constructed based on an ethogram of single elements of behaviour. Comparing flow charts for two gap sizes revealed differences in the frequency and succession of these elements, especially during the first part of the sequence.
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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
                17 November 2017
                November 2017
                : 5
                : 22 ( doiID: 10.1002/phy2.2017.5.issue-22 )
                : e13489
                Affiliations
                [ 1 ] Heisenberg Research Group of Computational Neuroscience – Modeling Neuronal Network Function University of Cologne Cologne Germany
                [ 2 ] Karolinska Institute University of Stockholm Stockholm Sweden
                [ 3 ] Institute of Neuroscience and Medicine (INM‐3) Research Center Jülich Jülich Germany
                Author notes
                [*] [* ] Correspondence

                Silvia Daun, Heisenberg Professor for Computational Neuroscience – Modeling Neural Network Function, Institute of Zoology, University of Cologne, D‐50674 Cologne, Germany.

                Phone: +49 221‐470‐3829

                Fax: +44 221‐470‐4889

                Email: silvia.daun@ 123456uni-koeln.de

                Article
                PHY213489
                10.14814/phy2.13489
                5704076
                29146863
                3f47555e-364e-4fd7-9f74-1cb66f395195
                © 2017 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 Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 21 June 2017
                : 03 October 2017
                : 10 October 2017
                Page count
                Figures: 11, Tables: 0, Pages: 17, Words: 9330
                Funding
                Funded by: Deutsche Forschungsgemeinschaft
                Award ID: DA1953/5‐2
                Award ID: GR3690/2‐1
                Award ID: GR3690/4‐1
                Categories
                Motor Control
                Skeletal Muscle
                Original Research
                Original Research
                Custom metadata
                2.0
                phy213489
                November 2017
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.2.6.1 mode:remove_FC converted:28.11.2017

                locomotion,motor control,neuromuscular model,stick insect

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