Physiology Department, Monash University, Clayton, Victoria 3800, Australia.
BMC Neurosci. 2013 Oct 7;14:114. doi: 10.1186/1471-2202-14-114.
A major cue for the position of a high-frequency sound source in azimuth is the difference in sound pressure levels in the two ears, Interaural Level Differences (ILDs), as a sound is presented from different positions around the head. This study aims to use data classification techniques to build a descriptive model of electro-physiologically determined neuronal sensitivity functions for ILDs. The ILDs were recorded from neurons in the central nucleus of the Inferior Colliculus (ICc), an obligatory midbrain auditory relay nucleus. The majority of ICc neurons (~ 85%) show sensitivity to ILDs but with a variety of different forms that are often difficult to unambiguously separate into different information-bearing types. Thus, this division is often based on laboratory-specific and relatively subjective criteria. Given the subjectivity and non-uniformity of ILD classification methods in use, we examined if objective data classification techniques for this purpose. Our key objectives were to determine if we could find an analytical method (A) to validate the presence of four typical ILD sensitivity functions as is commonly assumed in the field, and (B) whether this method produced classifications that mapped on to the physiologically observed results.
The three-step data classification procedure forms the basic methodology of this manuscript. In this three-step procedure, several data normalization techniques were first tested to select a suitable normalization technique to our data. This was then followed by PCA to reduce data dimensionality without losing the core characteristics of the data. Finally Cluster Analysis technique was applied to determine the number of clustered data with the aid of the CCC and Inconsistency Coefficient values.
The outcome of a three-step analytical data classification process was the identification of seven distinctive forms of ILD functions. These seven ILD function classes were found to map to the four "known" ideal ILD sensitivity function types, namely: Sigmoidal-EI, Sigmoidal-IE, Peaked, and Insensitive, ILD functions, and variations within these classes. This indicates that these seven templates can be utilized in future modelling studies.
We developed a taxonomy of ILD sensitivity functions using a methodological data classification approach. The number and types of generic ILD function patterns found with this method mapped well on to our electrophysiologically determined ILD sensitivity functions. While a larger data set of the latter functions may bring a more robust outcome, this good mapping is encouraging in providing a principled method for classifying such data sets, and could be well extended to other such neuronal sensitivity functions, such as contrast tuning in vision.
高频声源方位的主要线索是双耳之间的声压级差异（ILDs），因为声音是从头部周围的不同位置发出的。本研究旨在使用数据分类技术为电生理确定的 ILD 神经元敏感函数构建描述性模型。ILD 是从下丘脑中核（ICc）的神经元记录的，ICc 是一个强制性的中脑听觉中继核。大多数 ICc 神经元（~85%）对 ILD 敏感，但具有多种不同的形式，这些形式通常难以明确地分为不同的信息承载类型。因此，这种划分通常基于实验室特定的和相对主观的标准。鉴于 ILD 分类方法的主观性和非一致性，我们研究了是否可以使用客观的数据分类技术来解决这个问题。我们的主要目标是确定是否可以找到一种分析方法（A）来验证该领域通常假设的四种典型 ILD 敏感函数的存在，以及（B）该方法是否产生了与生理观察结果相对应的分类。
三步数据分类程序构成了本文的基本方法。在这个三步程序中，首先测试了几种数据归一化技术，以选择适合我们数据的归一化技术。然后进行主成分分析（PCA）以在不丢失数据核心特征的情况下降低数据维度。最后，借助一致性系数（CCC）和不一致系数值，应用聚类分析技术来确定聚类数据的数量。
三步分析数据分类过程的结果是确定了七种独特形式的 ILD 函数。发现这七种 ILD 函数类映射到四种“已知”理想 ILD 敏感函数类型，即：Sigmoidal-EI、Sigmoidal-IE、峰形和不敏感 ILD 函数，以及这些类中的变体。这表明这七种模板可以用于未来的建模研究。
我们使用基于方法论的数据分类方法开发了一种 ILD 敏感函数分类法。使用这种方法找到的 ILD 功能的数量和类型与我们的电生理确定的 ILD 敏感功能很好地吻合。虽然更大的后者函数数据集可能会带来更稳健的结果，但这种良好的映射令人鼓舞，它为分类此类数据集提供了一种原则性方法，并且可以很好地扩展到其他类似的神经元敏感函数，例如视觉中的对比度调谐。
