• DocumentCode
    1184543
  • Title

    First-Spike Rank Order as a Reliable Indicator of Burst Initiation and Its Relation With Early-to-Fire Neurons

  • Author

    Pan, Liangbin ; Song, Xindong ; Xiang, Guangxin ; Wong, Andy ; Xing, Wanli ; Cheng, Jing

  • Author_Institution
    Sch. of Med., Tsinghua Univ., Beijing
  • Volume
    56
  • Issue
    6
  • fYear
    2009
  • fDate
    6/1/2009 12:00:00 AM
  • Firstpage
    1673
  • Lastpage
    1682
  • Abstract
    In this paper, we study the spontaneous cortical neuronal network in hopes of finding a reliable indicator of burst initiation pathway, which would allow us to study burst initiation in conjunction with burst propagation in future research. Electrical activity is recorded using a 96-electrode microelectrode array on a weekly batch culture (half of the medium was replaced twice every week). We hypothesize that the first-spike onset sequence, which we call first-spike rank order (FSRO) is a reliable indicator of burst initiation, and verified our hypothesis by studying evoked bursts using rearranged rank probability matrices. Under similar conditions, stimulating the same site reliably reproduces the same FSRO. Spontaneous bursts can be classified based on their FSRO using dendrogram clustering. Bursts with different first-spike sequences showed evidence of sharing common early-to-fire neurons, but early-to-fire neurons only consist of a minority of neuronal activity during burst initiation, which is in partial accordance with existing literature. In the study of early-to-fire neurons, we also noticed that our batch-cultured network did not show clear preburst activity, which may indicate fundamental difference compared to continuous perfusion culture.
  • Keywords
    bioelectric phenomena; biological tissues; microelectrodes; neural nets; neurophysiology; pattern classification; pattern clustering; FSRO; batch-cultured network; burst initiation; burst initiation pathway; continuous perfusion culture; dendrogram clustering; early-to-fire neuron; electrical activity recording; first-spike rank order; microelectrode array; spontaneous burst classification; spontaneous cortical neuronal network; Biological neural networks; Biomedical engineering; Engineering in medicine and biology; Fires; In vitro; In vivo; Information analysis; Microelectrodes; Neurons; Performance analysis; Sequences; Systems biology; Burst initiation; early-to-fire neuron; multielectrode array (MEA); neuronal network; sequence; Algorithms; Animals; Cell Culture Techniques; Cluster Analysis; Electrophysiology; Microelectrodes; Nerve Net; Neurons; Rats; Rats, Sprague-Dawley; Signal Processing, Computer-Assisted;
  • fLanguage
    English
  • Journal_Title
    Biomedical Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9294
  • Type

    jour

  • DOI
    10.1109/TBME.2009.2015652
  • Filename
    4797857