Modelling spike trains and extracting response latency with Bayesian binning
Modelling spike trains and extracting response latency with Bayesian binning
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DOI：
10.1016/j.jphysparis.2009.11.015
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发表时间：
2010-05-01
影响因子：
--
通讯作者：
Oram, Mike W.
中科院分区：
文献类型：
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作者：
Endres, Dominik;Schindelin, Johannes;Oram, Mike W.
刺激周时间直方图（PSTH）和锋电位密度函数（SDF）是神经生理数据分析中常用的两种方法。PSTH通常通过对尖峰序列进行分箱来获得，SDF是PSTH的（高斯）核平滑版本。虽然箱宽度或核大小的选择通常相对任意，但最近已经尝试补救这种情况（Shimazaki和Shinomoto，2007 c，B，a）。我们进一步开发了一种精确的贝叶斯生成模型方法来估计PSTH（Endres等人，2008年），并证明其优越性，竞争的方法使用的数据，从早期（LGN）和晚期（STSa）的视觉领域。我们还强调了我们的计划的自动复杂性控制和生成的误差条的优势。此外，我们的方法允许提取的兴奋性和抑制性反应潜伏期从穗列车在原则上，无论是重复和单次试验数据。我们表明，该方法可以适用于高背景放电率和抑制反应（LGN）的数据，以及低放电率和兴奋性反应（STSa）的数据。此外，我们在模拟数据上证明了我们的延迟提取方法适用于一系列信噪比和背景发射率。虽然需要进一步的研究来检查我们的方法的敏感性，例如，在发射率和适应的逐渐变化，目前的结果表明，贝叶斯分箱是一个强大的方法估计的发射率和提取反应潜伏期从神经元尖峰列车。(C)2009爱思唯尔有限公司版权所有。
The peristimulus time histogram (PSTH) and the spike density function (SDF) are commonly used in the analysis of neurophysiological data. The PSTH is usually obtained by binning spike trains, the SDF being a (Gaussian) kernel smoothed version of the PSTH. While selection of the bin width or kernel size is often relatively arbitrary there have been recent attempts to remedy this situation (Shimazaki and Shinomoto, 2007c,b,a). We further develop an exact Bayesian generative model approach to estimating PSTHs (Endres et al., 2008) and demonstate its superiority to competing methods using data from early (LGN) and late (STSa) visual areas. We also highlight the advantages of our scheme's automatic complexity control and generation of error bars. Additionally, our approach allows extraction of excitatory and inhibitory response latency from spike trains in a principled way, both on repeated and single trial data. We show that the method can be applied to data with high background firing rates and inhibitory responses (LGN) as well as to data with low firing rate and excitatory responses (STSa). Furthermore, we demonstrate on simulated data that our latency extraction method works for a range of signal-to-noise ratios and background firing rates. While further studies are needed to examine the sensitivity of our method to, for example, gradual changes in firing rate and adaptation, the current results suggest that Bayesian binning is a powerful method for the estimation of firing rate and the extraction response latency from neuronal spike trains. (C) 2009 Elsevier Ltd. All rights reserved.