Feature extraction from spike trains with Bayesian binning: 'Latency is where the signal starts'
Feature extraction from spike trains with Bayesian binning: 'Latency is where the signal starts'
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DOI：
10.1007/s10827-009-0157-3
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发表时间：
2010-08-01
影响因子：
1.2
通讯作者：
Oram, Mike
中科院分区：
文献类型：
--
作者：
Endres, Dominik;Oram, Mike
刺激周时间直方图（PSTH）和它更连续的表亲——脉冲密度函数（SDF）是神经生理学家分析工具箱中的主要工具。前者通常通过对尖峰串进行分簇处理来获得，而后者的标准方法是使用高斯核进行平滑处理。尽管最近有人试图纠正这种情况(DiMatteo et al., Biometrika 88(4):1055-1071, 2001；Shimazaki and Shinomoto 2007a，神经网络计算19(6):1503- 1527,2007b, c；Cunningham et al. 2008)。我们开发了一种精确的贝叶斯生成模型方法来估计pshs。该方案的优点包括复杂度自动控制和预测误差条。我们展示了如何以一种原则性的方式对尖峰序列进行特征提取，通过对重复和单次试验数据的延迟和发射率后验分布评估来举例说明。我们还通过模拟和真实的神经元数据证明，我们的方法比目前的竞争方法提供了更准确的PSTH和延迟估计。我们利用后验分布对高水平视区STSa神经元的潜伏期和放电率组成的神经编码进行了信息论分析。我们的方法的软件实现可以在机器学习开源软件库（www.mloss.org，项目‘binsdfc’）上获得。
The peristimulus time histogram (PSTH) and its more continuous cousin, the spike density function (SDF) are staples in the analytic toolkit of neurophysiologists. The former is usually obtained by binning spike trains, whereas the standard method for the latter is smoothing with a Gaussian kernel. Selection of a bin width or a kernel size is often done in an relatively arbitrary fashion, even though there have been recent attempts to remedy this situation (DiMatteo et al., Biometrika 88(4):1055-1071, 2001; Shimazaki and Shinomoto 2007a, Neural Comput 19(6):1503-1527, 2007b, c; Cunningham et al. 2008). We develop an exact Bayesian, generative model approach to estimating PSTHs. Advantages of our scheme include automatic complexity control and error bars on its predictions. We show how to perform feature extraction on spike trains in a principled way, exemplified through latency and firing rate posterior distribution evaluations on repeated and single trial data. We also demonstrate using both simulated and real neuronal data that our approach provides a more accurate estimates of the PSTH and the latency than current competing methods. We employ the posterior distributions for an information theoretic analysis of the neural code comprised of latency and firing rate of neurons in high-level visual area STSa. A software implementation of our method is available at the machine learning open source software repository (www.mloss.org, project 'binsdfc').