neuroWalknet, a controller for hexapod walking allowing for context dependent behavior

Schilling M, Cruse H (2023)
PLoS Computational Biology 19(1): e1010136.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Autor*in
Schilling, Malte; Cruse, HolkUniBi
Abstract / Bemerkung
Decentralized control has been established as a key control principle in insect walking and has been successfully leveraged to account for a wide range of walking behaviors in the proposed neuroWalknet architecture. This controller allows for walking patterns at different velocities in both, forward and backward direction-quite similar to the behavior shown in stick insects-, for negotiation of curves, and for robustly dealing with various disturbances. While these simulations focus on the cooperation of different, decentrally controlled legs, here we consider a set of biological experiments not yet been tested by neuroWalknet, that focus on the function of the individual leg and are context dependent. These intraleg studies deal with four groups of interjoint reflexes. The reflexes are elicited by stimulation of the femoral chordotonal organ (fCO) or groups of campaniform sensilla (CS). Motor output signals are recorded from the alpha-joint, the beta-joint or the gamma-joint of the leg. Furthermore, the influence of these sensory inputs to artificially induced oscillations by application of pilocarpine has been studied. Although these biological data represent results obtained from different local reflexes in different contexts, they fit with and are embedded into the behavior shown by the global structure of neuroWalknet. In particular, a specific and intensively studied behavior, active reaction, has since long been assumed to represent a separate behavioral element, from which it is not clear why it occurs in some situations, but not in others. This question could now be explained as an emergent property of the holistic structure of neuroWalknet which has shown to be able to produce artificially elicited pilocarpine-driven oscillation that can be controlled by sensory input without the need of explicit innate CPG structures. As the simulation data result from a holistic system, further results were obtained that could be used as predictions to be tested in further biological experiments. Copyright: © 2023 Schilling, Cruse. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Erscheinungsjahr
2023
Zeitschriftentitel
PLoS Computational Biology
Band
19
Ausgabe
1
Art.-Nr.
e1010136
eISSN
1553-7358
Page URI
https://pub.uni-bielefeld.de/record/2968659

Zitieren

Schilling M, Cruse H. neuroWalknet, a controller for hexapod walking allowing for context dependent behavior. PLoS Computational Biology . 2023;19(1): e1010136.
Schilling, M., & Cruse, H. (2023). neuroWalknet, a controller for hexapod walking allowing for context dependent behavior. PLoS Computational Biology , 19(1), e1010136. https://doi.org/10.1371/journal.pcbi.1010136
Schilling, Malte, and Cruse, Holk. 2023. “neuroWalknet, a controller for hexapod walking allowing for context dependent behavior”. PLoS Computational Biology 19 (1): e1010136.
Schilling, M., and Cruse, H. (2023). neuroWalknet, a controller for hexapod walking allowing for context dependent behavior. PLoS Computational Biology 19:e1010136.
Schilling, M., & Cruse, H., 2023. neuroWalknet, a controller for hexapod walking allowing for context dependent behavior. PLoS Computational Biology , 19(1): e1010136.
M. Schilling and H. Cruse, “neuroWalknet, a controller for hexapod walking allowing for context dependent behavior”, PLoS Computational Biology , vol. 19, 2023, : e1010136.
Schilling, M., Cruse, H.: neuroWalknet, a controller for hexapod walking allowing for context dependent behavior. PLoS Computational Biology . 19, : e1010136 (2023).
Schilling, Malte, and Cruse, Holk. “neuroWalknet, a controller for hexapod walking allowing for context dependent behavior”. PLoS Computational Biology 19.1 (2023): e1010136.

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