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Capturing Spike Variability in Noisy Izhikevich Neurons Using Point Process Generalized Linear Models

机译:使用点过程广义线性模型捕获嘈杂的Izhikevich神经元中的尖峰变异性

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摘要

To understand neural activity, two broad categories of models exist: statistical and dynamical.While statistical models possess rigorous methods for parameter estimation and goodness-of-fit assessment, dynamical models provide mechanistic insight. In general, these two categories of models are separately applied; understanding the relationships between these modeling approaches remains an area of active research. In this letter, we examine this relationship using simulation. To do so, we first generate spike train data from a well-known dynamical model, the Izhikevich neuron, with a noisy input current. We then fit these spike train datawith a statistical model (a generalized linear model, GLM, with multiplicative influences of past spiking). For different levels of noise, we show how the GLM captures both the deterministic features of the Izhikevich neuron and the variability driven by the noise. We conclude that the GLM captures essential features of the simulated spike trains, but for near-deterministic spike trains, goodness-of-fit analyses reveal that the model does not fit very well in a statistical sense; the essential random part of the GLM is not captured.
机译:为了理解神经活动,存在两大类模型:统计模型和动力学模型。虽然统计模型具有用于参数估计和拟合优度评估的严格方法,但是动力学模型提供了机械的见解。一般而言,这两类模型是分别应用的。了解这些建模方法之间的关系仍然是积极研究的领域。在这封信中,我们使用模拟研究了这种关系。为此,我们首先从著名的动力学模型Izhikevich神经元生成带有噪声输入电流的峰值训练数据。然后,我们使用统计模型(广义线性模型GLM,具有过去峰值的乘积影响)拟合这些峰值训练数据。对于不同级别的噪声,我们将展示GLM如何捕获伊奇克维奇神经元的确定性特征以及噪声驱动的可变性。我们得出的结论是,GLM捕获了模拟峰值序列的基本特征,但是对于接近确定性的峰值序列,拟合优度分析表明,该模型在统计意义上不太适合。没有捕获GLM的基本随机部分。

著录项

  • 来源
    《Neural computation》 |2018年第1期|125-148|共24页
  • 作者单位

    University of Copenhagen, 2100 Copenhagen, Denmark;

    Boston University, Boston, MA 02215, U.S.A.;

    Boston University, Boston, MA 02215, 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
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