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Modeling the Respiratory Central Pattern Generator with Resonate-and-Fire Izhikevich-Neurons

  • Pavel Tolmachev
  • Rishi R. Dhingra
  • Michael Pauley
  • Mathias Dutschmann
  • Jonathan H. Manton
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11301)

Abstract

Computational models of the respiratory central pattern generator (rCPG) are usually based on biologically-plausible Hodgkin Huxley neuron models. Such models require numerous parameters and thus are prone to overfitting. The HH approach is motivated by the assumption that the biophysical properties of neurons determine the network dynamics. Here, we implement the rCPG using simpler Izhikevich resonate-and-fire neurons. Our rCPG model generates a 3-phase respiratory motor pattern based on established connectivities and can reproduce previous experimental and theoretical observations. Further, we demonstrate the flexibility of the model by testing whether intrinsic bursting properties are necessary for rhythmogenesis. Our simulations demonstrate that replacing predicted mandatory bursting properties of pre-inspiratory neurons with spike adapting properties yields a model that generates comparable respiratory activity patterns. The latter supports our view that the importance of the exact modeling parameters of specific respiratory neurons is overestimated.

Keywords

Respiratory central pattern generator Rhythm generation Resonate-and-fire neurons Brainstem 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Electrical and Electronic EngineeringUniversity of MelbourneParkvilleAustralia
  2. 2.Florey Institute of Neuroscience and Mental HealthParkvilleAustralia

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