Intrinsic bursters increase the robustness of rhythm generation in an excitatory network.
The pre-Botzinger complex (pBC) is a vital subcircuit of the respiratory central pattern generator. Although the existence of neurons with pacemaker-like bursting properties in this network is not questioned, their role in network rhythmogenesis is unresolved. Modeling is ideally suited to address this debate because of the ease with which biophysical parameters of individual cells and network architecture can be manipulated. We modeled the parameter variability of experimental data from pBC bursting pacemaker and nonpacemaker neurons using a modified version of our previously developed pBC neuron and network models. To investigate the role of pacemakers in networkwide rhythmogenesis, we simulated networks of these neurons and varied the fraction of the population made up of pacemakers. For each number of pacemaker neurons, we varied the amount of tonic drive to the network and measured the frequency of synchronous networkwide bursting produced. Both excitatory networks with all-to-all coupling and sparsely connected networks were explored for several levels of synaptic coupling strength. Networks containing only nonpacemakers were able to produce networkwide bursting, but with a low probability of bursting and low input and output ranges. Our results indicate that inclusion of pacemakers in an excitatory network increases robustness of the network by more than tripling the input and output ranges compared with networks containing no pacemakers. The largest increase in dynamic range occurs when the number of pacemakers in the network is greater than 20% of the population. Experimental tests of our model predictions are proposed.
Purvis
L K
LK
Laboratory for Neuroengineering and School of Electrical and Computer Engineering, Georgia Institute of Technology, 313 Ferst Drive, Atlanta, GA 30332-0535, USA.
Smith
J C
JC
Koizumi
H
H
Butera
R J
RJ
D000344:Afferent Pathways / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D000831:Animals, Newborn; D001683:Biological Clocks; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004594:Electrophysiology; D007700:Kinetics; D008959:Models, Neurological; D016571:Neural Networks, Computer; D051381:Rats; D009435:Synaptic Transmission / Q000502:physiology