title: Emergent Network Topology within the Respiratory Rhythm-Generating Kernel Evolved In Silico.
abstract: We hypothesize that the network topology within the pre-Bötzinger Complex (preBötC), the mammalian respiratory rhythm generating kernel, is not random, but is optimized in the course of ontogeny/phylogeny so that the network produces respiratory rhythm efficiently and robustly. In the present study, we attempted to identify topology of synaptic connections among constituent neurons of the preBötC based on this hypothesis. To do this, we first developed an effective evolutionary algorithm for optimizing network topology of a neuronal network to exhibit a 'desired characteristic'. Using this evolutionary algorithm, we iteratively evolved an in silico preBötC 'model' network with initial random connectivity to a network exhibiting optimized synchronous population bursts. The evolved 'idealized' network was then analyzed to gain insight into: (1) optimal network connectivity among different kinds of neurons-excitatory as well as inhibitory pacemakers, non-pacemakers and tonic neurons-within the preBötC, and (2) possible functional roles of inhibitory neurons within the preBötC in rhythm generation. Obtained results indicate that (1) synaptic distribution within excitatory subnetwork of the evolved model network illustrates skewed/heavy-tailed degree distribution, and (2) inhibitory subnetwork influences excitatory subnetwork primarily through non-tonic pacemaker inhibitory neurons. Further, since small-world (SW) network is generally associated with network synchronization phenomena and is suggested as a possible network structure within the preBötC, we compared the performance of SW network with that of the evolved model network. Results show that evolved network is better than SW network at exhibiting synchronous bursts.
authors.affiliation: Department of Biomedical Engineering, Peking University, Beijing, 100871 P. R. China.
authors.forename: Amit
authors.initials: A
authors.lastname: Lal
authors.affiliation: Department of Physiology, Hyogo College of Medicine, Nishinomiya, 663-8501 Japan.
authors.forename: Yoshitaka
authors.initials: Y
authors.lastname: Oku
authors.affiliation: School of Information Science and Technology, Tokai University, Hiratsuka, 259-1292 Japan.
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Someya
authors.affiliation: Department of Statistical Modeling, The Institute of Statistical Mathematics, Tachikawa, 190-8562 Japan.|Department of Statistical Science, School of Multidisciplinary Sciences, The Graduate University for Advanced Studies, Tachikawa, 190-8562, Japan.
authors.forename: Fumikazu
authors.initials: F
authors.lastname: Miwakeichi
authors.affiliation: Department of Statistical Modeling, The Institute of Statistical Mathematics, Tachikawa, 190-8562 Japan.|Department of Statistical Science, School of Multidisciplinary Sciences, The Graduate University for Advanced Studies, Tachikawa, 190-8562, Japan.
authors.forename: Yoshiyasu
authors.initials: Y
authors.lastname: Tamura
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D009415:Nerve Net; D012125:Respiratory Center / Q000502:physiology*