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Intrinsic properties of avian interaural level difference sound localizing neurons.

ABSTRACT: Intrinsic properties of neurons are one major determinant for how neurons respond to their synaptic inputs and shape their outputs in neural circuits. Here, we studied the intrinsic properties of neurons in the chicken posterior portion of the dorsal nucleus of the lateral lemniscus (LLDp), the first interaural level difference (ILD) encoder of the avian auditory pathway. Using whole-cell recordings in brain slices, we revealed that the LLDp is composed of a heterogeneous neuron population based on their firing patterns. LLDp neurons were broadly classified as either phasic or tonic firing neurons, with further classification applied to tonic firing neurons, such as regular (most dominant, n = 82 out of 125 cells, 65.6%), pauser, or adaptive firing. Neurons with different firing patterns were distributed about evenly across the dorsoventral as well as mediolateral axis of LLDp. Phasic firing neurons were of faster membrane time constant, and lower excitability than tonic firing neurons. The action potentials (APs) elicited at the current thresholds displayed significant differences in first spike latency, AP peak amplitude, half-width, and maximal rising and falling rates. Interestingly, for APs elicited at suprathreshold currents (400 pA above thresholds), some of the differences diminished while a few others emerged. Remarkably, most parameters of the APs at thresholds were significantly different from those of APs at suprathresholds. Combined with our previous study (Curry and Lu, 2016), the results lend support to the two-cell type model for ILD coding in the avian system.

SUBMITTER: Curry RJ

PROVIDER: S-EPMC7852074 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundA 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.MethodsThe 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.ResultsThe 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.ConclusionsWe 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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Intrinsic properties of avian interaural level difference sound localizing neurons.

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