Neuronal spike trains are used by the nervous system to encode and transmit information. Euclidean distance-based methods (EDBMs) have been applied to quantify the similarity between temporally-discretized spike trains and model responses. In this study, using the same discretization procedure, we developed and applied a joint probability-based method (JPBM) to classify individual spike trains of slowly adapting pulmonary stretch receptors (SARs). The activity of individual SARs was recorded in anaesthetized, paralysed adult male rabbits, which were artificially-ventilated at constant rate and one of three different volumes. Two-thirds of the responses to the 600 stimuli presented at each volume were used to construct three response models (one for each stimulus volume) consisting of a series of time bins, each with spike probabilities. The remaining one-third of the responses where used as test responses to be classified into one of the three model responses. This was done by computing the joint probability of observing the same series of events (spikes or no spikes, dictated by the test response) in a given model and determining which probability of the three was highest. The JPBM generally produced better classification accuracy than the EDBM, and both performed well above chance. Both methods were similarly affected by variations in discretization parameters, response epoch duration, and two different response alignment strategies. Increasing bin widths increased classification accuracy, which also improved with increased observation time, but primarily during periods of increasing lung inflation. Thus, the JPBM is a simple and effective method performing spike train classification.

中文翻译：

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基于联合概率的神经元穗序列分类

神经元尖峰序列被神经系统用来编码和传输信息。基于欧氏距离的方法（EDBM）已用于量化时间离散的尖峰序列和模型响应之间的相似性。在这项研究中，我们使用相同的离散化程序，开发并应用了基于联合概率的方法（JPBM）对慢适应性肺拉伸受体（SAR）的单个峰值序列进行分类。在麻醉，瘫痪的成年雄性兔子中记录单个SAR的活性，这些兔子以恒定的速率和三种不同容积之一进行人工换气。在每个体积上呈现的对600个刺激的响应的三分之二用于构建三个响应模型（每个刺激体积一个），该模型由一系列时间仓组成，每个时间仓都有尖峰概率。其余的三分之一响应用作测试响应，被分类为三个模型响应之一。这是通过计算在给定模型中观察到相同系列事件（由测试响应决定的尖峰或无尖峰）的联合概率，并确定三个事件中哪个概率最高来实现的。JPBM通常比EDBM产生更好的分类准确性，并且两者的表现都远高于偶然。离散化参数，响应历时长和两种不同的响应对齐策略的变化对两种方法的影响相似。箱宽度的增加增加了分类的准确性，这也随着观察时间的增加而提高，但主要是在肺膨胀增加的时期。从而，