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Distinct neuronal types contribute to hybrid temporal encoding strategies in primate auditory cortex

Studies of the encoding of sensory stimuli by the brain often consider recorded neurons as a pool of identical units. Here, we report divergence in stimulus-encoding properties between subpopulations of cortical neurons that are classified based on spike timing and waveform features. Neurons in audi...

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Autores principales: Liu, Xiao-Ping, Wang, Xiaoqin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9132345/
https://www.ncbi.nlm.nih.gov/pubmed/35613218
http://dx.doi.org/10.1371/journal.pbio.3001642
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author Liu, Xiao-Ping
Wang, Xiaoqin
author_facet Liu, Xiao-Ping
Wang, Xiaoqin
author_sort Liu, Xiao-Ping
collection PubMed
description Studies of the encoding of sensory stimuli by the brain often consider recorded neurons as a pool of identical units. Here, we report divergence in stimulus-encoding properties between subpopulations of cortical neurons that are classified based on spike timing and waveform features. Neurons in auditory cortex of the awake marmoset (Callithrix jacchus) encode temporal information with either stimulus-synchronized or nonsynchronized responses. When we classified single-unit recordings using either a criteria-based or an unsupervised classification method into regular-spiking, fast-spiking, and bursting units, a subset of intrinsically bursting neurons formed the most highly synchronized group, with strong phase-locking to sinusoidal amplitude modulation (SAM) that extended well above 20 Hz. In contrast with other unit types, these bursting neurons fired primarily on the rising phase of SAM or the onset of unmodulated stimuli, and preferred rapid stimulus onset rates. Such differentiating behavior has been previously reported in bursting neuron models and may reflect specializations for detection of acoustic edges. These units responded to natural stimuli (vocalizations) with brief and precise spiking at particular time points that could be decoded with high temporal stringency. Regular-spiking units better reflected the shape of slow modulations and responded more selectively to vocalizations with overall firing rate increases. Population decoding using time-binned neural activity found that decoding behavior differed substantially between regular-spiking and bursting units. A relatively small pool of bursting units was sufficient to identify the stimulus with high accuracy in a manner that relied on the temporal pattern of responses. These unit type differences may contribute to parallel and complementary neural codes.
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spelling pubmed-91323452022-05-26 Distinct neuronal types contribute to hybrid temporal encoding strategies in primate auditory cortex Liu, Xiao-Ping Wang, Xiaoqin PLoS Biol Research Article Studies of the encoding of sensory stimuli by the brain often consider recorded neurons as a pool of identical units. Here, we report divergence in stimulus-encoding properties between subpopulations of cortical neurons that are classified based on spike timing and waveform features. Neurons in auditory cortex of the awake marmoset (Callithrix jacchus) encode temporal information with either stimulus-synchronized or nonsynchronized responses. When we classified single-unit recordings using either a criteria-based or an unsupervised classification method into regular-spiking, fast-spiking, and bursting units, a subset of intrinsically bursting neurons formed the most highly synchronized group, with strong phase-locking to sinusoidal amplitude modulation (SAM) that extended well above 20 Hz. In contrast with other unit types, these bursting neurons fired primarily on the rising phase of SAM or the onset of unmodulated stimuli, and preferred rapid stimulus onset rates. Such differentiating behavior has been previously reported in bursting neuron models and may reflect specializations for detection of acoustic edges. These units responded to natural stimuli (vocalizations) with brief and precise spiking at particular time points that could be decoded with high temporal stringency. Regular-spiking units better reflected the shape of slow modulations and responded more selectively to vocalizations with overall firing rate increases. Population decoding using time-binned neural activity found that decoding behavior differed substantially between regular-spiking and bursting units. A relatively small pool of bursting units was sufficient to identify the stimulus with high accuracy in a manner that relied on the temporal pattern of responses. These unit type differences may contribute to parallel and complementary neural codes. Public Library of Science 2022-05-25 /pmc/articles/PMC9132345/ /pubmed/35613218 http://dx.doi.org/10.1371/journal.pbio.3001642 Text en © 2022 Liu, Wang https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Liu, Xiao-Ping
Wang, Xiaoqin
Distinct neuronal types contribute to hybrid temporal encoding strategies in primate auditory cortex
title Distinct neuronal types contribute to hybrid temporal encoding strategies in primate auditory cortex
title_full Distinct neuronal types contribute to hybrid temporal encoding strategies in primate auditory cortex
title_fullStr Distinct neuronal types contribute to hybrid temporal encoding strategies in primate auditory cortex
title_full_unstemmed Distinct neuronal types contribute to hybrid temporal encoding strategies in primate auditory cortex
title_short Distinct neuronal types contribute to hybrid temporal encoding strategies in primate auditory cortex
title_sort distinct neuronal types contribute to hybrid temporal encoding strategies in primate auditory cortex
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9132345/
https://www.ncbi.nlm.nih.gov/pubmed/35613218
http://dx.doi.org/10.1371/journal.pbio.3001642
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