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Time as a supervisor: temporal regularity and auditory object learning
Sensory systems appear to learn to transform incoming sensory information into perceptual representations, or “objects,” that can inform and guide behavior with minimal explicit supervision. Here, we propose that the auditory system can achieve this goal by using time as a supervisor, i.e., by learn...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10192587/ https://www.ncbi.nlm.nih.gov/pubmed/37216064 http://dx.doi.org/10.3389/fncom.2023.1150300 |
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author | DiTullio, Ronald W. Parthiban, Chetan Piasini, Eugenio Chaudhari, Pratik Balasubramanian, Vijay Cohen, Yale E. |
author_facet | DiTullio, Ronald W. Parthiban, Chetan Piasini, Eugenio Chaudhari, Pratik Balasubramanian, Vijay Cohen, Yale E. |
author_sort | DiTullio, Ronald W. |
collection | PubMed |
description | Sensory systems appear to learn to transform incoming sensory information into perceptual representations, or “objects,” that can inform and guide behavior with minimal explicit supervision. Here, we propose that the auditory system can achieve this goal by using time as a supervisor, i.e., by learning features of a stimulus that are temporally regular. We will show that this procedure generates a feature space sufficient to support fundamental computations of auditory perception. In detail, we consider the problem of discriminating between instances of a prototypical class of natural auditory objects, i.e., rhesus macaque vocalizations. We test discrimination in two ethologically relevant tasks: discrimination in a cluttered acoustic background and generalization to discriminate between novel exemplars. We show that an algorithm that learns these temporally regular features affords better or equivalent discrimination and generalization than conventional feature-selection algorithms, i.e., principal component analysis and independent component analysis. Our findings suggest that the slow temporal features of auditory stimuli may be sufficient for parsing auditory scenes and that the auditory brain could utilize these slowly changing temporal features. |
format | Online Article Text |
id | pubmed-10192587 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101925872023-05-19 Time as a supervisor: temporal regularity and auditory object learning DiTullio, Ronald W. Parthiban, Chetan Piasini, Eugenio Chaudhari, Pratik Balasubramanian, Vijay Cohen, Yale E. Front Comput Neurosci Neuroscience Sensory systems appear to learn to transform incoming sensory information into perceptual representations, or “objects,” that can inform and guide behavior with minimal explicit supervision. Here, we propose that the auditory system can achieve this goal by using time as a supervisor, i.e., by learning features of a stimulus that are temporally regular. We will show that this procedure generates a feature space sufficient to support fundamental computations of auditory perception. In detail, we consider the problem of discriminating between instances of a prototypical class of natural auditory objects, i.e., rhesus macaque vocalizations. We test discrimination in two ethologically relevant tasks: discrimination in a cluttered acoustic background and generalization to discriminate between novel exemplars. We show that an algorithm that learns these temporally regular features affords better or equivalent discrimination and generalization than conventional feature-selection algorithms, i.e., principal component analysis and independent component analysis. Our findings suggest that the slow temporal features of auditory stimuli may be sufficient for parsing auditory scenes and that the auditory brain could utilize these slowly changing temporal features. Frontiers Media S.A. 2023-05-04 /pmc/articles/PMC10192587/ /pubmed/37216064 http://dx.doi.org/10.3389/fncom.2023.1150300 Text en Copyright © 2023 DiTullio, Parthiban, Piasini, Chaudhari, Balasubramanian and Cohen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience DiTullio, Ronald W. Parthiban, Chetan Piasini, Eugenio Chaudhari, Pratik Balasubramanian, Vijay Cohen, Yale E. Time as a supervisor: temporal regularity and auditory object learning |
title | Time as a supervisor: temporal regularity and auditory object learning |
title_full | Time as a supervisor: temporal regularity and auditory object learning |
title_fullStr | Time as a supervisor: temporal regularity and auditory object learning |
title_full_unstemmed | Time as a supervisor: temporal regularity and auditory object learning |
title_short | Time as a supervisor: temporal regularity and auditory object learning |
title_sort | time as a supervisor: temporal regularity and auditory object learning |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10192587/ https://www.ncbi.nlm.nih.gov/pubmed/37216064 http://dx.doi.org/10.3389/fncom.2023.1150300 |
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