Neural networks control complex motor outputs by generating several rhythmic neuronal activities, often with different time scales. One example of such a network is the pre-Bötzinger complex respiratory network (preBötC) that can simultaneously generate fast, small-amplitude, monophasic eupneic breaths together with slow, high-amplitude, biphasic augmented breaths (sighs). However, the underlying rhythmogenic mechanisms for this bimodal discharge pattern remain unclear, leaving two possible explanations: the existence of either reconfiguring processes within the same network or two distinct subnetworks. Based on recent in vitro data obtained in the mouse embryo, we have built a computational model consisting of two compartments, interconnected through appropriate synapses. One compartment generates sighs and the other produces eupneic bursts. The model reproduces basic features of simultaneous sigh and eupnea generation (two types of bursts differing in terms of shape, amplitude, and frequency of occurrence) and mimics the effect of blocking glycinergic synapses. Furthermore, we used this model to make predictions that were subsequently tested on the isolated preBötC in mouse brainstem slice preparations. Through a combination of in vitro and in silico approaches we find that (1) sigh events are less sensitive to network excitability than eupneic activity, (2) calcium-dependent mechanisms and the Ih current play a prominent role in sigh generation, and (3) specific parameters of Ih activation set the low sensitivity to excitability in the sigh neuronal subset. Altogether, our results strongly support the hypothesis that distinct subpopulations within the preBötC network are responsible for sigh and eupnea rhythmogenesis.
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