...
首页> 外文期刊>Neural computation >Dynamics of Membrane Excitability Determine Interspike Interval Variability: A Link Between Spike Generation Mechanisms and Cortical Spike Train Statistics
【24h】

Dynamics of Membrane Excitability Determine Interspike Interval Variability: A Link Between Spike Generation Mechanisms and Cortical Spike Train Statistics

机译:膜兴奋性的动力学决定穗间期的变异性:穗生成机制和皮质穗列车统计之间的联系。

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       

摘要

We propose a biophysical mechanism for the high interspike interval variability observed in cortical spike trains. The key lies in the nonlinear dynamics of cortical spike generation, which are consistent with type I membranes where saddle-node dynamics underlie excitability (Rinzel & Ermentrout, 1989). We present a canonical model for type I membranes, the θ-neuron. The θ-neuron is a phase model whose dynamics reflect salient features of type I membranes. This model generates spike trains with coefficient of variation (CV) above 0.6 when brought to firing by noisy inputs. This happens because the timing of spikes for a type I excitable cell is exquisitely sensitive to the amplitude of the suprathreshold stimulus pulses. A noisy input current, giving random amplitude “kicks” to the cell, evokes highly irregular firing across a wide range of firing rates; an intrinsically oscillating cell gives regular spike trains. We corroborate the results with simulations of the Morris-Lecar (M-L) neural model with random synaptic inputs: type I M-L yields high CVs. When this model is modified to have type II dynamics (periodicity arises via a Hopf bifurcation), however, it gives regular spike trains (CV below 0.3). Our results suggest that the high CV values such as those observed in cortical spike trains are an intrinsic characteristic of type I membranes driven to firing by “random” inputs. In contrast, neural oscillators or neurons exhibiting type II excitability should produce regular spike trains.
机译:我们提出了一种生物物理机制,用于在皮秒杀序列中观察到高的突突间期变异性。关键在于皮质尖峰生成的非线性动力学,这与I型膜一致,其中鞍点动力学是激发性的基础(Rinzel&Ermentrout,1989)。我们提出了I型膜的经典模型θ-神经元。 θ神经元是一个相模型,其动力学反映了I型膜的显着特征。当由噪声输入触发时,该模型会生成变异系数(CV)大于0.6的峰值串。之所以会发生这种情况,是因为I型可激发细胞的尖峰时间对阈上刺激脉冲的幅度非常敏感。有噪声的输入电流给电池提供了随机的幅度“拐点”,在很宽的发射速率范围内会引起高度不规则的发射。一个固有的振荡单元会产生规律的峰值运动。我们通过具有随机突触输入的Morris-Lecar(M-L)神经模型的仿真来证实结果:I M-L型产生高CV。但是,当将此模型修改为具有II型动力学(周期性通过Hopf分叉产生)时,它会提供规则的峰值运动(CV低于0.3)。我们的结果表明,较高的CV值(例如在皮质峰值序列中观察到的CV值)是由“随机”输入驱动发射的I型膜的固有特征。相反,表现出II型兴奋性的神经振荡器或神经元应产生规则的尖峰序列。

著录项

  • 来源
    《Neural computation》 |1998年第5期|1047-1065|共19页
  • 作者

    Gutkin B; Ermentrout G;

  • 作者单位

    Program in Neurobiology and Department of Mathematics, University of Pittsburgh, Pittsburgh, PA 15260, U.S.A.;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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