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Modular structure in C. elegans neural network and its response to external localized stimuli

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  • Moreira, Carolina A.
  • de Aguiar, Marcus A.M.

Abstract

Synchronization plays a key role in information processing in neuronal networks. Response of specific groups of neurons are triggered by external stimuli, such as visual, tactile or olfactory inputs. Neurons, however, can be divided into several categories, such as by physical location, functional role or topological clustering properties. Here we study the response of the electric junction C. elegans network to external stimuli using the partially forced Kuramoto model and applying the force to specific groups of neurons. Stimuli were applied to three topological modules, two ganglia, specified by their anatomical localization, and to the functional groups composed of all sensory and motoneurons. We found that topological modules do not contain purely anatomical groups or functional classes, corroborating previous results, and that stimulating different classes of neurons lead to very different responses, measured in terms of synchronization and phase velocity correlations. In all cases the modular structure hindered full synchronization, protecting the system from seizures. The responses to stimuli applied to topological and functional modules showed pronounced patterns of correlation or anti-correlation with other modules that were not observed when the stimulus was applied to a ganglion with mixed functional neurons.

Suggested Citation

  • Moreira, Carolina A. & de Aguiar, Marcus A.M., 2019. "Modular structure in C. elegans neural network and its response to external localized stimuli," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 533(C).
  • Handle: RePEc:eee:phsmap:v:533:y:2019:i:c:s0378437119311938
    DOI: 10.1016/j.physa.2019.122051
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    Cited by:

    1. Fariello, Ricardo & de Aguiar, Marcus A.M., 2024. "Exploring the phase diagrams of multidimensional Kuramoto models," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).
    2. Barioni, Ana Elisa D. & de Aguiar, Marcus A.M., 2021. "Complexity reduction in the 3D Kuramoto model," Chaos, Solitons & Fractals, Elsevier, vol. 149(C).

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