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Computational Neural Modeling of Auditory Cortical Receptive Fields

Previous studies have shown that the auditory cortex can enhance the perception of behaviorally important sounds in the presence of background noise, but the mechanisms by which it does this are not yet elucidated. Rapid plasticity of spectrotemporal receptive fields (STRFs) in the primary (A1) cort...

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Autores principales: Chambers, Jordan D., Elgueda, Diego, Fritz, Jonathan B., Shamma, Shihab A., Burkitt, Anthony N., Grayden, David B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6543553/
https://www.ncbi.nlm.nih.gov/pubmed/31178710
http://dx.doi.org/10.3389/fncom.2019.00028
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author Chambers, Jordan D.
Elgueda, Diego
Fritz, Jonathan B.
Shamma, Shihab A.
Burkitt, Anthony N.
Grayden, David B.
author_facet Chambers, Jordan D.
Elgueda, Diego
Fritz, Jonathan B.
Shamma, Shihab A.
Burkitt, Anthony N.
Grayden, David B.
author_sort Chambers, Jordan D.
collection PubMed
description Previous studies have shown that the auditory cortex can enhance the perception of behaviorally important sounds in the presence of background noise, but the mechanisms by which it does this are not yet elucidated. Rapid plasticity of spectrotemporal receptive fields (STRFs) in the primary (A1) cortical neurons is observed during behavioral tasks that require discrimination of particular sounds. This rapid task-related change is believed to be one of the processing strategies utilized by the auditory cortex to selectively attend to one stream of sound in the presence of mixed sounds. However, the mechanism by which the brain evokes this rapid plasticity in the auditory cortex remains unclear. This paper uses a neural network model to investigate how synaptic transmission within the cortical neuron network can change the receptive fields of individual neurons. A sound signal was used as input to a model of the cochlea and auditory periphery, which activated or inhibited integrate-and-fire neuron models to represent networks in the primary auditory cortex. Each neuron in the network was tuned to a different frequency. All neurons were interconnected with excitatory or inhibitory synapses of varying strengths. Action potentials in one of the model neurons were used to calculate the receptive field using reverse correlation. The results were directly compared to previously recorded electrophysiological data from ferrets performing behavioral tasks that require discrimination of particular sounds. The neural network model could reproduce complex STRFs observed experimentally through optimizing the synaptic weights in the model. The model predicts that altering synaptic drive between cortical neurons and/or bottom-up synaptic drive from the cochlear model to the cortical neurons can account for rapid task-related changes observed experimentally in A1 neurons. By identifying changes in the synaptic drive during behavioral tasks, the model provides insights into the neural mechanisms utilized by the auditory cortex to enhance the perception of behaviorally salient sounds.
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spelling pubmed-65435532019-06-07 Computational Neural Modeling of Auditory Cortical Receptive Fields Chambers, Jordan D. Elgueda, Diego Fritz, Jonathan B. Shamma, Shihab A. Burkitt, Anthony N. Grayden, David B. Front Comput Neurosci Neuroscience Previous studies have shown that the auditory cortex can enhance the perception of behaviorally important sounds in the presence of background noise, but the mechanisms by which it does this are not yet elucidated. Rapid plasticity of spectrotemporal receptive fields (STRFs) in the primary (A1) cortical neurons is observed during behavioral tasks that require discrimination of particular sounds. This rapid task-related change is believed to be one of the processing strategies utilized by the auditory cortex to selectively attend to one stream of sound in the presence of mixed sounds. However, the mechanism by which the brain evokes this rapid plasticity in the auditory cortex remains unclear. This paper uses a neural network model to investigate how synaptic transmission within the cortical neuron network can change the receptive fields of individual neurons. A sound signal was used as input to a model of the cochlea and auditory periphery, which activated or inhibited integrate-and-fire neuron models to represent networks in the primary auditory cortex. Each neuron in the network was tuned to a different frequency. All neurons were interconnected with excitatory or inhibitory synapses of varying strengths. Action potentials in one of the model neurons were used to calculate the receptive field using reverse correlation. The results were directly compared to previously recorded electrophysiological data from ferrets performing behavioral tasks that require discrimination of particular sounds. The neural network model could reproduce complex STRFs observed experimentally through optimizing the synaptic weights in the model. The model predicts that altering synaptic drive between cortical neurons and/or bottom-up synaptic drive from the cochlear model to the cortical neurons can account for rapid task-related changes observed experimentally in A1 neurons. By identifying changes in the synaptic drive during behavioral tasks, the model provides insights into the neural mechanisms utilized by the auditory cortex to enhance the perception of behaviorally salient sounds. Frontiers Media S.A. 2019-05-24 /pmc/articles/PMC6543553/ /pubmed/31178710 http://dx.doi.org/10.3389/fncom.2019.00028 Text en Copyright © 2019 Chambers, Elgueda, Fritz, Shamma, Burkitt and Grayden. http://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
Chambers, Jordan D.
Elgueda, Diego
Fritz, Jonathan B.
Shamma, Shihab A.
Burkitt, Anthony N.
Grayden, David B.
Computational Neural Modeling of Auditory Cortical Receptive Fields
title Computational Neural Modeling of Auditory Cortical Receptive Fields
title_full Computational Neural Modeling of Auditory Cortical Receptive Fields
title_fullStr Computational Neural Modeling of Auditory Cortical Receptive Fields
title_full_unstemmed Computational Neural Modeling of Auditory Cortical Receptive Fields
title_short Computational Neural Modeling of Auditory Cortical Receptive Fields
title_sort computational neural modeling of auditory cortical receptive fields
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6543553/
https://www.ncbi.nlm.nih.gov/pubmed/31178710
http://dx.doi.org/10.3389/fncom.2019.00028
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