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Targeted dimensionality reduction enables reliable estimation of neural population coding accuracy from trial-limited data

Rapidly developing technology for large scale neural recordings has allowed researchers to measure the activity of hundreds to thousands of neurons at single cell resolution in vivo. Neural decoding analyses are a widely used tool used for investigating what information is represented in this comple...

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Detalles Bibliográficos
Autores principales: Heller, Charles R., David, Stephen V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9302847/
https://www.ncbi.nlm.nih.gov/pubmed/35862300
http://dx.doi.org/10.1371/journal.pone.0271136
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author Heller, Charles R.
David, Stephen V.
author_facet Heller, Charles R.
David, Stephen V.
author_sort Heller, Charles R.
collection PubMed
description Rapidly developing technology for large scale neural recordings has allowed researchers to measure the activity of hundreds to thousands of neurons at single cell resolution in vivo. Neural decoding analyses are a widely used tool used for investigating what information is represented in this complex, high-dimensional neural population activity. Most population decoding methods assume that correlated activity between neurons has been estimated accurately. In practice, this requires large amounts of data, both across observations and across neurons. Unfortunately, most experiments are fundamentally constrained by practical variables that limit the number of times the neural population can be observed under a single stimulus and/or behavior condition. Therefore, new analytical tools are required to study neural population coding while taking into account these limitations. Here, we present a simple and interpretable method for dimensionality reduction that allows neural decoding metrics to be calculated reliably, even when experimental trial numbers are limited. We illustrate the method using simulations and compare its performance to standard approaches for dimensionality reduction and decoding by applying it to single-unit electrophysiological data collected from auditory cortex.
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spelling pubmed-93028472022-07-22 Targeted dimensionality reduction enables reliable estimation of neural population coding accuracy from trial-limited data Heller, Charles R. David, Stephen V. PLoS One Research Article Rapidly developing technology for large scale neural recordings has allowed researchers to measure the activity of hundreds to thousands of neurons at single cell resolution in vivo. Neural decoding analyses are a widely used tool used for investigating what information is represented in this complex, high-dimensional neural population activity. Most population decoding methods assume that correlated activity between neurons has been estimated accurately. In practice, this requires large amounts of data, both across observations and across neurons. Unfortunately, most experiments are fundamentally constrained by practical variables that limit the number of times the neural population can be observed under a single stimulus and/or behavior condition. Therefore, new analytical tools are required to study neural population coding while taking into account these limitations. Here, we present a simple and interpretable method for dimensionality reduction that allows neural decoding metrics to be calculated reliably, even when experimental trial numbers are limited. We illustrate the method using simulations and compare its performance to standard approaches for dimensionality reduction and decoding by applying it to single-unit electrophysiological data collected from auditory cortex. Public Library of Science 2022-07-21 /pmc/articles/PMC9302847/ /pubmed/35862300 http://dx.doi.org/10.1371/journal.pone.0271136 Text en © 2022 Heller, David 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
Heller, Charles R.
David, Stephen V.
Targeted dimensionality reduction enables reliable estimation of neural population coding accuracy from trial-limited data
title Targeted dimensionality reduction enables reliable estimation of neural population coding accuracy from trial-limited data
title_full Targeted dimensionality reduction enables reliable estimation of neural population coding accuracy from trial-limited data
title_fullStr Targeted dimensionality reduction enables reliable estimation of neural population coding accuracy from trial-limited data
title_full_unstemmed Targeted dimensionality reduction enables reliable estimation of neural population coding accuracy from trial-limited data
title_short Targeted dimensionality reduction enables reliable estimation of neural population coding accuracy from trial-limited data
title_sort targeted dimensionality reduction enables reliable estimation of neural population coding accuracy from trial-limited data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9302847/
https://www.ncbi.nlm.nih.gov/pubmed/35862300
http://dx.doi.org/10.1371/journal.pone.0271136
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