title: Robust network oscillations during mammalian respiratory rhythm generation driven by synaptic dynamics.
abstract: How might synaptic dynamics generate synchronous oscillations in neuronal networks? We address this question in the preBötzinger complex (preBötC), a brainstem neural network that paces robust, yet labile, inspiration in mammals. The preBötC is composed of a few hundred neurons that alternate bursting activity with silent periods, but the mechanism underlying this vital rhythm remains elusive. Using a computational approach to model a randomly connected neuronal network that relies on short-term synaptic facilitation (SF) and depression (SD), we show that synaptic fluctuations can initiate population activities through recurrent excitation. We also show that a two-step SD process allows activity in the network to synchronize (bursts) and generate a population refractory period (silence). The model was validated against an array of experimental conditions, which recapitulate several processes the preBötC may experience. Consistent with the modeling assumptions, we reveal, by electrophysiological recordings, that SF/SD can occur at preBötC synapses on timescales that influence rhythmic population activity. We conclude that nondeterministic neuronal spiking and dynamic synaptic strengths in a randomly connected network are sufficient to give rise to regular respiratory-like rhythmic network activity and lability, which may play an important role in generating the rhythm for breathing and other coordinated motor activities in mammals.
authors.affiliation: Group of Applied Mathematics and Computational Biology, IBENS, Ecole Normale Supérieure, 75005 Paris, France;
authors.forename: Claire
authors.initials: C
authors.lastname: Guerrier
authors.affiliation: UMR9107, CNRS/Université Paris-Sud, Institut des Neurosciences Paris-Saclay, Université Paris-Saclay, F-91190 Gif-sur Yvette, France.
authors.forename: John A
authors.identifier: http://orcid.org/0000-0003-2849-7672
authors.initials: JA
authors.lastname: Hayes
authors.affiliation: UMR9107, CNRS/Université Paris-Sud, Institut des Neurosciences Paris-Saclay, Université Paris-Saclay, F-91190 Gif-sur Yvette, France.
authors.forename: Gilles
authors.initials: G
authors.lastname: Fortin
authors.affiliation: Group of Applied Mathematics and Computational Biology, IBENS, Ecole Normale Supérieure, 75005 Paris, France; david.holcman@ens.fr.
authors.forename: David
authors.initials: D
authors.lastname: Holcman
mesh_terms: D000818:Animals; D008322:Mammals / Q000502:physiology*; D008564:Membrane Potentials; D051379:Mice; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009473:Neuronal Plasticity; D009474:Neurons / Q000502:physiology; D010507:Periodicity; D012125:Respiratory Center / Q000502:physiology*; D013569:Synapses / Q000502:physiology*; D013997:Time Factors
keywords: breathing; central pattern generator; mathematical modeling; numerical simulations; synaptic depression