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Inferring the basis of binaural detection with a modified autoencoder

Smith, Samuel; Sollini, Joseph; Akeroyd, Michael A.

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Assistant Professor


The binaural system utilizes interaural timing cues to improve the detection of auditory signals presented in noise. In humans, the binaural mechanisms underlying this phenomenon cannot be directly measured and hence remain contentious. As an alternative, we trained modified autoencoder networks to mimic human-like behavior in a binaural detection task. The autoencoder architecture emphasizes interpretability and, hence, we “opened it up” to see if it could infer latent mechanisms underlying binaural detection. We found that the optimal networks automatically developed artificial neurons with sensitivity to timing cues and with dynamics consistent with a cross-correlation mechanism. These computations were similar to neural dynamics reported in animal models. That these computations emerged to account for human hearing attests to their generality as a solution for binaural signal detection. This study examines the utility of explanatory-driven neural network models and how they may be used to infer mechanisms of audition.

Journal Article Type Article
Acceptance Date Jan 2, 2023
Online Publication Date Jan 26, 2023
Publication Date Jan 26, 2023
Deposit Date Jan 4, 2023
Publicly Available Date Jan 26, 2023
Journal Frontiers in Neuroscience
Print ISSN 1662-4548
Electronic ISSN 1662-453X
Publisher Frontiers Media SA
Peer Reviewed Peer Reviewed
Volume 17
Article Number 1000079
Keywords General Neuroscience
Public URL
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