Biophysical Modeling of preBötzinger Complex Type-1 Neuron

Abstract

Breathing is a rhythmic motor behavior that regulates the exchange of blood gases. The preBötzinger Complex (preBötC) generates the inspiratory rhythm. preBötC rhythm is an emergent network property where synaptic connectivity and intrinsic neuronal properties play critical roles. Network synchronization in each breath cycle is necessary for preBötC rhythmogenesis. preBötC Type-1 neurons are putatively rhythmogenic. They synchronize with initial low-level activity due to convergent coincident inputs from their pre-synaptic partners. However, we still lack an understanding of how preBötC Type-1 neurons can act as coincident detectors since the Type-1 neurons are considered integrators. In this study, I developed experimentally constrained and validated models of preBötC Type-1 neurons to understand their physiology and computations. I constructed biophysical models of Type-1 neurons through a stochastic search spanning 24 model parameters and screened them for nine physiological measurements to ensure they were within their experimentally reported bounds. Of over two lakh models, 135 models qualified as physiologically valid. We observed that degenerate interaction among model parameters gave rise to robust Type-1 phenotype and underlies their heterogeneity. Type-1 models exhibited a strong correlation between the A-type K + channel and the sodium persistent channel properties. Published single-cell (patch-sequencing) transcriptomics data validate this model prediction. Spike-triggered averaged currents of valid models revealed they span the entire spectrum of encoding regimes from integrators to coincidence detectors. We postulate that the heterogeneity in physiological and encoding characteristics of Type-1 neurons is crucial for the resilience and robustness of preBötC dynamics and breathing.