Modeling the respiratory Central Pattern Generator with resonate-and-fire Izhikevich-Neurons

TL;DR

This paper presents a simplified computational model of the respiratory central pattern generator using resonate-and-fire neurons, demonstrating that complex bursting properties are not essential for rhythm generation.

Contribution

The study introduces a novel rCPG model based on Izhikevich resonate-and-fire neurons, challenging the necessity of detailed biophysical parameters for respiratory rhythmogenesis.

Findings

The model reproduces established respiratory patterns.

Replacing bursting with spike adaptation still yields functional rhythms.

Simpler neuron models can effectively simulate complex biological networks.

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.