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The discrimination of interaural level difference sensitivity functions: development of a taxonomic data template for modelling.

Uragun B, Rajan R - BMC Neurosci (2013)

Bottom Line: This was then followed by PCA to reduce data dimensionality without losing the core characteristics of the data.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Physiology Department, Monash University, Clayton, Victoria 3800, Australia. uragun@hotmail.com.

ABSTRACT

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

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Seven types of ILD functions observed. Typical four ideal ILD functions (Figure 8) can easily be perceived among these seven type of ILD functions here; what makes the another three “transitional” cluster findings is significantly important in this study. Type of ILD functions are derived from each clustered data by averaging their objects. All maximum numbers of mean spike counts is scaled up to 45 for a comparison reason. For example, The Cluster-4 shows peak type ILD functions by averaging its (25) objects where the Cluster-6 also shows arisen-peak ILD functions by averaging its (11) objects. These numbers of objects are also shown in Figure 9.
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Figure 7: Seven types of ILD functions observed. Typical four ideal ILD functions (Figure 8) can easily be perceived among these seven type of ILD functions here; what makes the another three “transitional” cluster findings is significantly important in this study. Type of ILD functions are derived from each clustered data by averaging their objects. All maximum numbers of mean spike counts is scaled up to 45 for a comparison reason. For example, The Cluster-4 shows peak type ILD functions by averaging its (25) objects where the Cluster-6 also shows arisen-peak ILD functions by averaging its (11) objects. These numbers of objects are also shown in Figure 9.

Mentions: Cluster Analysis yielded seven clusters of data, each containing a number of objects as shown in the dendrogram in Figure 6. We then averaged the objects in each cluster so as to represent the common data characteristics of each cluster with a mean ILD function for that cluster. Figure 7 shows the generic form of the ILD function found in each of these seven clusters; the type of ILD function in each cluster was derived by averaging the ILD functions (the “objects”) making up each cluster. The four-prototypical ILD functions generally reported in the literature (see Figure 8) can easily be perceived among the seven types of ILD functions shown here. The three “new” ILD function types found here are “transition” ILD function and represent the novel finding of significance in this study.


The discrimination of interaural level difference sensitivity functions: development of a taxonomic data template for modelling.

Uragun B, Rajan R - BMC Neurosci (2013)

Seven types of ILD functions observed. Typical four ideal ILD functions (Figure 8) can easily be perceived among these seven type of ILD functions here; what makes the another three “transitional” cluster findings is significantly important in this study. Type of ILD functions are derived from each clustered data by averaging their objects. All maximum numbers of mean spike counts is scaled up to 45 for a comparison reason. For example, The Cluster-4 shows peak type ILD functions by averaging its (25) objects where the Cluster-6 also shows arisen-peak ILD functions by averaging its (11) objects. These numbers of objects are also shown in Figure 9.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4126173&req=5

Figure 7: Seven types of ILD functions observed. Typical four ideal ILD functions (Figure 8) can easily be perceived among these seven type of ILD functions here; what makes the another three “transitional” cluster findings is significantly important in this study. Type of ILD functions are derived from each clustered data by averaging their objects. All maximum numbers of mean spike counts is scaled up to 45 for a comparison reason. For example, The Cluster-4 shows peak type ILD functions by averaging its (25) objects where the Cluster-6 also shows arisen-peak ILD functions by averaging its (11) objects. These numbers of objects are also shown in Figure 9.
Mentions: Cluster Analysis yielded seven clusters of data, each containing a number of objects as shown in the dendrogram in Figure 6. We then averaged the objects in each cluster so as to represent the common data characteristics of each cluster with a mean ILD function for that cluster. Figure 7 shows the generic form of the ILD function found in each of these seven clusters; the type of ILD function in each cluster was derived by averaging the ILD functions (the “objects”) making up each cluster. The four-prototypical ILD functions generally reported in the literature (see Figure 8) can easily be perceived among the seven types of ILD functions shown here. The three “new” ILD function types found here are “transition” ILD function and represent the novel finding of significance in this study.

Bottom Line: This was then followed by PCA to reduce data dimensionality without losing the core characteristics of the data.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Physiology Department, Monash University, Clayton, Victoria 3800, Australia. uragun@hotmail.com.

ABSTRACT

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Show MeSH
Related in: MedlinePlus