title: A network model comprising 4 segmental, interconnected ganglia, and its application to simulate multi-legged locomotion in crustaceans.
abstract: Inter-segmental coordination is crucial for the locomotion of animals. Arthropods show high variability of leg numbers, from 6 in insects up to 750 legs in millipedes. Despite this fact, the anatomical and functional organization of their nervous systems show basic similarities. The main similarities are the segmental organization, and the way the function of the segmental units is coordinated. We set out to construct a model that could describe locomotion (walking) in animals with more than 6 legs, as well as in 6-legged animals (insects). To this end, we extended a network model by Daun-Gruhn and Tóth (Journal of Computational Neuroscience, doi: 10.1007/s10827-010-0300-1 , 2011). This model describes inter-segmental coordination of the ipsilateral legs in the stick insect during walking. Including an additional segment (local network) into the original model, we could simulate coordination patterns that occur in animals walking on eight legs (e.g., crayfish). We could improve the model by modifying its original cyclic connection topology. In all model variants, the phase relations between the afferent segmental excitatory sensory signals and the oscillatory activity of the segmental networks played a crucial role. Our results stress the importance of this sensory input on the generation of different stable coordination patterns. The simulations confirmed that using the modified connection topology, the flexibility of the model behaviour increased, meaning that changing a single phase parameter, i.e., gating properties of just one afferent sensory signal was sufficient to reproduce all coordination patterns seen in the experiments.
authors.affiliation: Heisenberg Research Group of Computational Biology, Department of Animal Physiology, Institute of Zoology, University of Cologne, Zülpicher Str. 47b, 50674, Cologne, Germany.
authors.forename: M
authors.initials: M
authors.lastname: Grabowska
authors.forename: T I
authors.initials: TI
authors.lastname: Toth
authors.forename: C
authors.initials: C
authors.lastname: Smarandache-Wellmann
authors.forename: S
authors.initials: S
authors.lastname: Daun-Gruhn
mesh_terms: D000818:Animals; D063226:Central Pattern Generators; D003198:Computer Simulation; D003445:Crustacea / Q000502:physiology*; D025461:Feedback, Physiological; D017952:Ganglia, Invertebrate / Q000502:physiology*; D007313:Insecta / Q000502:physiology; D008124:Locomotion / Q000502:physiology*; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000166:cytology / Q000502:physiology; D012677:Sensation / Q000502:physiology; D013569:Synapses / Q000502:physiology; D016138:Walking / Q000502:physiology