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Predicting the Influence of Axon Myelination on Sound Localization Precision Using a Spiking Neural Network Model of Auditory Brainstem
Li, Ben Zheng1,2,3,4; Pun, Sio Hang3; Vai, Mang I.3,4; Lei, Tim C.1,2; Klug, Achim1
2022-03-14
Source PublicationFrontiers in Neuroscience
ISSN1662-4548
Volume16Issue:840983
Abstract

Spatial hearing allows animals to rapidly detect and localize auditory events in the surrounding environment. The auditory brainstem plays a central role in processing and extracting binaural spatial cues through microsecond-precise binaural integration, especially for detecting interaural time differences (ITDs) of low-frequency sounds at the medial superior olive (MSO). A series of mechanisms exist in the underlying neural circuits for preserving accurate action potential timing across multiple fibers, synapses and nuclei along this pathway. One of these is the myelination of afferent fibers that ensures reliable and temporally precise action potential propagation in the axon. There are several reports of fine-tuned myelination patterns in the MSO circuit, but how specifically myelination influences the precision of sound localization remains incompletely understood. Here we present a spiking neural network (SNN) model of the Mongolian gerbil auditory brainstem with myelinated axons to investigate whether different axon myelination thicknesses alter the sound localization process. Our model demonstrates that axon myelin thickness along the contralateral pathways can substantially modulate ITD detection. Furthermore, optimal ITD sensitivity is reached when the MSO receives contralateral inhibition via thicker myelinated axons compared to contralateral excitation, a result that is consistent with previously reported experimental observations. Our results suggest specific roles of axon myelination for extracting temporal dynamics in ITD decoding, especially in the pathway of the contralateral inhibition.

KeywordSound Localization Auditory Brainstem Medial Superior Olive Myelin Alteration Interaural Time Difference Spiking Neural Network Computational Model
DOI10.3389/fnins.2022.840983
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaNeurosciences & Neurology
WOS SubjectNeurosciences
WOS IDWOS:000778535200001
Scopus ID2-s2.0-85127608715
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Citation statistics
Document TypeJournal article
CollectionDEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING
THE STATE KEY LABORATORY OF ANALOG AND MIXED-SIGNAL VLSI (UNIVERSITY OF MACAU)
Corresponding AuthorKlug, Achim
Affiliation1.Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus, Aurora, United States
2.Department of Electrical Engineering, University of Colorado, Denver, Denver, United States
3.State Key Laboratory of Analog and Mixed Signal Very-Large-Scale Integration (VLSI), University of Macau, Taipa, Macao
4.Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macao
First Author AffilicationUniversity of Macau;  Faculty of Science and Technology
Recommended Citation
GB/T 7714
Li, Ben Zheng,Pun, Sio Hang,Vai, Mang I.,et al. Predicting the Influence of Axon Myelination on Sound Localization Precision Using a Spiking Neural Network Model of Auditory Brainstem[J]. Frontiers in Neuroscience, 2022, 16(840983).
APA Li, Ben Zheng., Pun, Sio Hang., Vai, Mang I.., Lei, Tim C.., & Klug, Achim (2022). Predicting the Influence of Axon Myelination on Sound Localization Precision Using a Spiking Neural Network Model of Auditory Brainstem. Frontiers in Neuroscience, 16(840983).
MLA Li, Ben Zheng,et al."Predicting the Influence of Axon Myelination on Sound Localization Precision Using a Spiking Neural Network Model of Auditory Brainstem".Frontiers in Neuroscience 16.840983(2022).
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