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Tytuł:
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Neural rate difference model can account for lateralization of high-frequency stimuli.
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Autorzy:
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Klug J; Department of Medical Physics and Acoustics, University of Oldenburg, 26129 Oldenburg, Germany.
Schmors L; Department of Medical Physics and Acoustics, University of Oldenburg, 26129 Oldenburg, Germany.
Ashida G; Department of Neuroscience, University of Oldenburg, 26129 Oldenburg, Germany.
Dietz M; Department of Medical Physics and Acoustics, University of Oldenburg, 26129 Oldenburg, Germany.
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Źródło:
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The Journal of the Acoustical Society of America [J Acoust Soc Am] 2020 Aug; Vol. 148 (2), pp. 678.
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Typ publikacji:
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Journal Article; Research Support, Non-U.S. Gov't
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Język:
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English
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Imprint Name(s):
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Publication: Melville, NY : American Institute of Physics
Original Publication: Lancaster, Pa. [etc.] : American Institute of Physics for the Acoustical Society of America
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MeSH Terms:
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Sound Localization*
Acoustic Stimulation ; Functional Laterality ; Humans ; Neurons ; Sound
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Entry Date(s):
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Date Created: 20200903 Date Completed: 20210621 Latest Revision: 20210621
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Update Code:
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20240104
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DOI:
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10.1121/10.0001602
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PMID:
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32873019
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Lateralization of complex high-frequency sounds is conveyed by interaural level differences (ILDs) and interaural time differences (ITDs) in the envelope. In this work, the authors constructed an auditory model and simulate data from three previous behavioral studies obtained with, in total, over 1000 different amplitude-modulated stimuli. The authors combine a well-established auditory periphery model with a functional count-comparison model for binaural excitatory-inhibitory (EI) interaction. After parameter optimization of the EI-model stage, the hemispheric rate-difference between pairs of EI-model neurons relates linearly with the extent of laterality in human listeners. If a certain ILD and a certain envelope ITD each cause a similar extent of laterality, they also produce a similar rate difference in the same model neurons. After parameter optimization, the model accounts for 95.7% of the variance in the largest dataset, in which amplitude modulation depth, rate of modulation, modulation exponent, ILD, and envelope ITD were varied. The model also accounts for 83% of the variances in each of the other two datasets using the same EI model parameters.