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Erschienen in: Journal of Computational Neuroscience 2/2011

01.04.2011

A mathematical modeling study of inter-segmental coordination during stick insect walking

verfasst von: Silvia Daun-Gruhn

Erschienen in: Journal of Computational Neuroscience | Ausgabe 2/2011

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Abstract

The biomechanical conditions for walking in the stick insect require a modeling approach that is based on the control of pairs of antagonistic motoneuron (MN) pools for each leg joint by independent central pattern generators (CPGs). Each CPG controls a pair of antagonistic MN pools. Furthermore, specific sensory feedback signals play an important role in the control of single leg movement and in the generation of inter-leg coordination or the interplay between both tasks. Currently, however, no mathematical model exists that provides a theoretical approach to understanding the generation of coordinated locomotion in such a multi-legged locomotor system. In the present study, I created such a theoretical model for the stick insect walking system, which describes the MN activity of a single forward stepping middle leg and helps to explain the neuronal mechanisms underlying coordinating information transfer between ipsilateral legs. In this model, CPGs that belong to the same leg, as well as those belonging to different legs, are connected by specific sensory feedback pathways that convey information about movements and forces generated during locomotion. The model emphasizes the importance of sensory feedback, which is used by the central nervous system to enhance weak excitatory and inhibitory synaptic connections from front to rear between the three thorax-coxa-joint CPGs. Thereby the sensory feedback activates caudal pattern generation networks and helps to coordinate leg movements by generating in-phase and out-of-phase thoracic MN activity.

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Metadaten
Titel
A mathematical modeling study of inter-segmental coordination during stick insect walking
verfasst von
Silvia Daun-Gruhn
Publikationsdatum
01.04.2011
Verlag
Springer US
Erschienen in
Journal of Computational Neuroscience / Ausgabe 2/2011
Print ISSN: 0929-5313
Elektronische ISSN: 1573-6873
DOI
https://doi.org/10.1007/s10827-010-0254-3

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