Emergent Network Topology within the Respiratory Rhythm-Generating   Kernel Evolved In Silico

doi:10.1371/journal.pone.0154049

CORC > 北京大学 > 工学院

	Emergent Network Topology within the Respiratory Rhythm-Generating Kernel Evolved In Silico
	Lal, Amit ; Oku, Yoshitaka ; Someya, Hiroshi ; Miwakeichi, Fumikazu ; Tamura, Yoshiyasu
刊名	PLOS ONE
	2016
关键词	PRE-BOTZINGER COMPLEX PACEMAKER NEURONS PREBOTZINGER COMPLEX BUILDING-BLOCKS NEURAL-NETWORKS MODELS MICROCIRCUITS MOUSE CONNECTIVITY TRANSMISSION
DOI	10.1371/journal.pone.0154049
英文摘要	We hypothesize that the network topology within the pre-Botzinger Complex (preBotC), 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 preBotC 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 preBotC '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 preBotC, and (2) possible functional roles of inhibitory neurons within the preBotC 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 preBotC, 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.; Institute of Statistical Mathematics cooperative research program [2011-ISM-CRP-2001, 2012-ISM-CRP-2001, 2013-ISM-CRP-2001, 2014-ISM-CRP-2001]; JST (Japan Science and Technology Agency) Strategic Japanese-German Cooperative Program in Computational Neuroscience [12000005]; Japan Society for Promotion of Science [24300108, 24500365]; JST Strategic Japanese-German Cooperative Program in Computational Neuroscience [12000005]; SCI(E); PubMed; ARTICLE; yoku@hyo-med.ac.jp; 5; e0154049; 11
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/433833]
专题	工学院
推荐引用方式 GB/T 7714	Lal, Amit,Oku, Yoshitaka,Someya, Hiroshi,et al. Emergent Network Topology within the Respiratory Rhythm-Generating Kernel Evolved In Silico[J]. PLOS ONE,2016.
APA	Lal, Amit,Oku, Yoshitaka,Someya, Hiroshi,Miwakeichi, Fumikazu,&Tamura, Yoshiyasu.(2016).Emergent Network Topology within the Respiratory Rhythm-Generating Kernel Evolved In Silico.PLOS ONE.
MLA	Lal, Amit,et al."Emergent Network Topology within the Respiratory Rhythm-Generating Kernel Evolved In Silico".PLOS ONE (2016).