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The Essential Complexity of Auditory Receptive Fields

Encoding properties of sensory neurons are commonly modeled using linear finite impulse response (FIR) filters. For the auditory system, the FIR filter is instantiated in the spectro-temporal receptive field (STRF), often in the framework of the generalized linear model. Despite widespread use of th...

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Autores principales: Thorson, Ivar L., Liénard, Jean, David, Stephen V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4684325/
https://www.ncbi.nlm.nih.gov/pubmed/26683490
http://dx.doi.org/10.1371/journal.pcbi.1004628
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author Thorson, Ivar L.
Liénard, Jean
David, Stephen V.
author_facet Thorson, Ivar L.
Liénard, Jean
David, Stephen V.
author_sort Thorson, Ivar L.
collection PubMed
description Encoding properties of sensory neurons are commonly modeled using linear finite impulse response (FIR) filters. For the auditory system, the FIR filter is instantiated in the spectro-temporal receptive field (STRF), often in the framework of the generalized linear model. Despite widespread use of the FIR STRF, numerous formulations for linear filters are possible that require many fewer parameters, potentially permitting more efficient and accurate model estimates. To explore these alternative STRF architectures, we recorded single-unit neural activity from auditory cortex of awake ferrets during presentation of natural sound stimuli. We compared performance of > 1000 linear STRF architectures, evaluating their ability to predict neural responses to a novel natural stimulus. Many were able to outperform the FIR filter. Two basic constraints on the architecture lead to the improved performance: (1) factorization of the STRF matrix into a small number of spectral and temporal filters and (2) low-dimensional parameterization of the factorized filters. The best parameterized model was able to outperform the full FIR filter in both primary and secondary auditory cortex, despite requiring fewer than 30 parameters, about 10% of the number required by the FIR filter. After accounting for noise from finite data sampling, these STRFs were able to explain an average of 40% of A1 response variance. The simpler models permitted more straightforward interpretation of sensory tuning properties. They also showed greater benefit from incorporating nonlinear terms, such as short term plasticity, that provide theoretical advances over the linear model. Architectures that minimize parameter count while maintaining maximum predictive power provide insight into the essential degrees of freedom governing auditory cortical function. They also maximize statistical power available for characterizing additional nonlinear properties that limit current auditory models.
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spelling pubmed-46843252015-12-31 The Essential Complexity of Auditory Receptive Fields Thorson, Ivar L. Liénard, Jean David, Stephen V. PLoS Comput Biol Research Article Encoding properties of sensory neurons are commonly modeled using linear finite impulse response (FIR) filters. For the auditory system, the FIR filter is instantiated in the spectro-temporal receptive field (STRF), often in the framework of the generalized linear model. Despite widespread use of the FIR STRF, numerous formulations for linear filters are possible that require many fewer parameters, potentially permitting more efficient and accurate model estimates. To explore these alternative STRF architectures, we recorded single-unit neural activity from auditory cortex of awake ferrets during presentation of natural sound stimuli. We compared performance of > 1000 linear STRF architectures, evaluating their ability to predict neural responses to a novel natural stimulus. Many were able to outperform the FIR filter. Two basic constraints on the architecture lead to the improved performance: (1) factorization of the STRF matrix into a small number of spectral and temporal filters and (2) low-dimensional parameterization of the factorized filters. The best parameterized model was able to outperform the full FIR filter in both primary and secondary auditory cortex, despite requiring fewer than 30 parameters, about 10% of the number required by the FIR filter. After accounting for noise from finite data sampling, these STRFs were able to explain an average of 40% of A1 response variance. The simpler models permitted more straightforward interpretation of sensory tuning properties. They also showed greater benefit from incorporating nonlinear terms, such as short term plasticity, that provide theoretical advances over the linear model. Architectures that minimize parameter count while maintaining maximum predictive power provide insight into the essential degrees of freedom governing auditory cortical function. They also maximize statistical power available for characterizing additional nonlinear properties that limit current auditory models. Public Library of Science 2015-12-18 /pmc/articles/PMC4684325/ /pubmed/26683490 http://dx.doi.org/10.1371/journal.pcbi.1004628 Text en © 2015 Thorson et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Thorson, Ivar L.
Liénard, Jean
David, Stephen V.
The Essential Complexity of Auditory Receptive Fields
title The Essential Complexity of Auditory Receptive Fields
title_full The Essential Complexity of Auditory Receptive Fields
title_fullStr The Essential Complexity of Auditory Receptive Fields
title_full_unstemmed The Essential Complexity of Auditory Receptive Fields
title_short The Essential Complexity of Auditory Receptive Fields
title_sort essential complexity of auditory receptive fields
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4684325/
https://www.ncbi.nlm.nih.gov/pubmed/26683490
http://dx.doi.org/10.1371/journal.pcbi.1004628
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