ArticleFrontiers in neuroscience2018
Extraction of Inter-Aural Time Differences Using a Spiking Neuron Network Model of the Medial Superior Olive.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 16 citations in OpenAlex.
- Computational Model for Synthesizing Auditory Brainstem Responses to Assess Neuronal Alterations in Aging and Autistic Animal Models.Journal of the Association for Research in Otolaryngology : JARO · 2026Article
- Impacts of heminode disruption on auditory processing of noisy sound stimuli.bioRxiv : the preprint server for biology · 2026Article
- A hemispheric two-channel code accounts for binaural unmasking in humans.Communications biology · 2022Article
- Predicting the Influence of Axon Myelination on Sound Localization Precision Using a Spiking Neural Network Model of Auditory Brainstem.Frontiers in neuroscience · 2022Article
- Binaural Processing Deficits Due to Synaptopathy and Myelin Defects.Frontiers in neural circuits · 2022Article
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2 authors at 1 institution in 1 country.
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Abstract
The mammalian auditory system is able to extract temporal and spectral features from sound signals at the two ears. One important cue for localization of low-frequency sound sources in the horizontal plane are inter-aural time differences (ITDs) which are first analyzed in the medial superior olive (MSO) in the brainstem. Neural recordings of ITD tuning curves at various stages along the auditory pathway suggest that ITDs in the mammalian brainstem are not represented in form of a Jeffress-type place code. An alternative is the hemispheric opponent-channel code, according to which ITDs are encoded as the difference in the responses of the MSO nuclei in the two hemispheres. In this study, we present a physiologically-plausible, spiking neuron network model of the mammalian MSO circuit and apply two different methods of extracting ITDs from arbitrary sound signals. The network model is driven by a functional model of the auditory periphery and physiological models of the cochlear nucleus and the MSO. Using a linear opponent-channel decoder, we show that the network is able to detect changes in ITD with a precision down to 10 μs and that the sensitivity of the decoder depends on the slope of the ITD-rate functions. A second approach uses an artificial neuronal network to predict ITDs directly from the spiking output of the MSO and ANF model. Using this predictor, we show that the MSO-network is able to reliably encode static and time-dependent ITDs over a large frequency range, also for complex signals like speech.
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