• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Computational Neuroscience >Coarse-grained event tree analysis for quantifying Hodgkin-Huxley neuronal network dynamics
【24h】

Coarse-grained event tree analysis for quantifying Hodgkin-Huxley neuronal network dynamics

机译：粗粒度事件树分析用于量化Hodgkin-Huxley神经元网络动力学

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

We present an event tree analysis of studying the dynamics of the Hodgkin-Huxley (HH) neuronal networks. Our study relies on a coarse-grained projection to event trees and to the event chains that comprise these trees by using a statistical collection of spatial-temporal sequences of relevant physiological observables (such as sequences of spiking multiple neurons). This projection can retain information about network dynamics that covers multiple features, swiftly and robustly. We demonstrate that for even small differences in inputs, some dynamical regimes of HH networks contain sufficiently higher order statistics as reflected in event chains within the event tree analysis. Therefore, this analysis is effective in discriminating small differences in inputs. Moreover, we use event trees to analyze the results computed from an efficient library-based numerical method proposed in our previous work, where a pre-computed high resolution data library of typical neuronal trajectories during the interval of an action potential (spike) allows us to avoid resolving the spikes in detail. In this way, we can evolve the HH networks using time steps one order of magnitude larger than the typical time steps used for resolving the trajectories without the library, while achieving comparable statistical accuracy in terms of average firing rate and power spectra of voltage traces. Our numerical simulation results show that the library method is efficient in the sense that the results generated by using this numerical method with much larger time steps contain sufficiently high order statistical structure of firing events that are similar to the ones obtained using a regular HH solver. We use our event tree analysis to demonstrate these statistical similarities.
机译：我们提出一个事件树分析，研究霍奇金-赫克斯利（HH）神经元网络的动力学。我们的研究依赖于对事件树以及包括这些树的事件链的粗粒度投影，方法是使用相关生理可观察物的时空序列（例如加标多个神经元的序列）的统计集合。这种预测可以快速，稳健地保留有关网络动态的信息，该信息涵盖多个功能。我们证明，即使输入的微小差异，HH网络的某些动态机制也包含足够高阶的统计信息，这反映在事件树分析中的事件链中。因此，此分析可有效区分输入中的细微差异。此外，我们使用事件树来分析从先前工作中提出的基于库的高效数值方法计算出的结果，其中在动作电位（尖峰）间隔期间预先计算的典型神经元轨迹的高分辨率数据库允许我们以避免详细解决峰值问题。通过这种方式，我们可以使用比用于在没有库的情况下解决轨迹的典型时间步长一个数量级大的时间步长来演化HH网络，同时在平均点火速率和电压迹线的功率谱方面达到可比的统计精度。我们的数值模拟结果表明，库方法是有效的，因为使用这种数值方法以更大的时间步长生成的结果包含足够高阶的触发事件统计结构，类似于使用常规HH求解器获得的结果。我们使用事件树分析来证明这些统计相似性。

著录项

  • 来源
    《Journal of Computational Neuroscience》 |2012年第1期|p.55-72|共18页
  • 作者

    Yi Sun; Aaditya V. Rangan; Douglas Zhou; David Cai;

  • 作者单位

    Statistical and Applied Mathematical Sciences Institute,19 T.W. Alexander Drive, P.O. Box 14006,Research Triangle Park, NC 27709, USA;

    Courant Institute of Mathematical Sciences and Center for Neural Science, New York University,New York, NY 10012, USA;

    Department of Mathematics and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China;

    Courant Institute of Mathematical Sciences and Center for Neural Science, New York University,New York, NY 10012, USA,Department of Mathematics and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    event tree analysis; information transmission; hodgkin-huxley neuronal network; library method; neuronal coding;

    机译：事件树分析;信息传递;霍奇金-赫x黎神经网络;库方法;神经元编码;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号