Residential College | false |
Status | 已發表Published |
Predicting the Influence of Axon Myelination on Sound Localization Precision Using a Spiking Neural Network Model of Auditory Brainstem | |
Ben-Zheng Li; Sio Hang Pun; Mang I Vai; Tim C Lei; Achim Klug | |
2022-03 | |
Source Publication | Front Neurosci |
Volume | 16Pages: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. |
Keyword | Sound Localization Auditory Brainstem Medial Superior Olive Myelin Alteration Interaural Time Difference Spiking Neural Network Computational Model |
URL | View the original |
Indexed By | SCIE |
Document Type | Journal article |
Collection | Faculty of Science and Technology |
Corresponding Author | Ben-Zheng Li |
Affiliation | 1.University of Macau 2.University of Macau 3.University of Macau 4.University of Colorado 5.University of Colorado |
Recommended Citation GB/T 7714 | Ben-Zheng Li,Sio Hang Pun,Mang I Vai,et al. Predicting the Influence of Axon Myelination on Sound Localization Precision Using a Spiking Neural Network Model of Auditory Brainstem[J]. Front Neurosci, 2022, 16, 840983. |
APA | Ben-Zheng Li., Sio Hang Pun., Mang I Vai., Tim C Lei., & Achim Klug (2022). Predicting the Influence of Axon Myelination on Sound Localization Precision Using a Spiking Neural Network Model of Auditory Brainstem. Front Neurosci, 16, 840983. |
MLA | Ben-Zheng Li,et al."Predicting the Influence of Axon Myelination on Sound Localization Precision Using a Spiking Neural Network Model of Auditory Brainstem".Front Neurosci 16(2022):840983. |
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