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Estimating the dimensionality of the manifold underlying multi-electrode neural recordings

It is generally accepted that the number of neurons in a given brain area far exceeds the number of neurons needed to carry any specific function controlled by that area. For example, motor areas of the human brain contain tens of millions of neurons that control the activation of tens or at most hu...

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Autores principales: Altan, Ege, Solla, Sara A., Miller, Lee E., Perreault, Eric J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8659648/
https://www.ncbi.nlm.nih.gov/pubmed/34843461
http://dx.doi.org/10.1371/journal.pcbi.1008591
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author Altan, Ege
Solla, Sara A.
Miller, Lee E.
Perreault, Eric J.
author_facet Altan, Ege
Solla, Sara A.
Miller, Lee E.
Perreault, Eric J.
author_sort Altan, Ege
collection PubMed
description It is generally accepted that the number of neurons in a given brain area far exceeds the number of neurons needed to carry any specific function controlled by that area. For example, motor areas of the human brain contain tens of millions of neurons that control the activation of tens or at most hundreds of muscles. This massive redundancy implies the covariation of many neurons, which constrains the population activity to a low-dimensional manifold within the space of all possible patterns of neural activity. To gain a conceptual understanding of the complexity of the neural activity within a manifold, it is useful to estimate its dimensionality, which quantifies the number of degrees of freedom required to describe the observed population activity without significant information loss. While there are many algorithms for dimensionality estimation, we do not know which are well suited for analyzing neural activity. The objective of this study was to evaluate the efficacy of several representative algorithms for estimating the dimensionality of linearly and nonlinearly embedded data. We generated synthetic neural recordings with known intrinsic dimensionality and used them to test the algorithms’ accuracy and robustness. We emulated some of the important challenges associated with experimental data by adding noise, altering the nature of the embedding of the low-dimensional manifold within the high-dimensional recordings, varying the dimensionality of the manifold, and limiting the amount of available data. We demonstrated that linear algorithms overestimate the dimensionality of nonlinear, noise-free data. In cases of high noise, most algorithms overestimated the dimensionality. We thus developed a denoising algorithm based on deep learning, the “Joint Autoencoder”, which significantly improved subsequent dimensionality estimation. Critically, we found that all algorithms failed when the intrinsic dimensionality was high (above 20) or when the amount of data used for estimation was low. Based on the challenges we observed, we formulated a pipeline for estimating the dimensionality of experimental neural data.
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spelling pubmed-86596482021-12-10 Estimating the dimensionality of the manifold underlying multi-electrode neural recordings Altan, Ege Solla, Sara A. Miller, Lee E. Perreault, Eric J. PLoS Comput Biol Research Article It is generally accepted that the number of neurons in a given brain area far exceeds the number of neurons needed to carry any specific function controlled by that area. For example, motor areas of the human brain contain tens of millions of neurons that control the activation of tens or at most hundreds of muscles. This massive redundancy implies the covariation of many neurons, which constrains the population activity to a low-dimensional manifold within the space of all possible patterns of neural activity. To gain a conceptual understanding of the complexity of the neural activity within a manifold, it is useful to estimate its dimensionality, which quantifies the number of degrees of freedom required to describe the observed population activity without significant information loss. While there are many algorithms for dimensionality estimation, we do not know which are well suited for analyzing neural activity. The objective of this study was to evaluate the efficacy of several representative algorithms for estimating the dimensionality of linearly and nonlinearly embedded data. We generated synthetic neural recordings with known intrinsic dimensionality and used them to test the algorithms’ accuracy and robustness. We emulated some of the important challenges associated with experimental data by adding noise, altering the nature of the embedding of the low-dimensional manifold within the high-dimensional recordings, varying the dimensionality of the manifold, and limiting the amount of available data. We demonstrated that linear algorithms overestimate the dimensionality of nonlinear, noise-free data. In cases of high noise, most algorithms overestimated the dimensionality. We thus developed a denoising algorithm based on deep learning, the “Joint Autoencoder”, which significantly improved subsequent dimensionality estimation. Critically, we found that all algorithms failed when the intrinsic dimensionality was high (above 20) or when the amount of data used for estimation was low. Based on the challenges we observed, we formulated a pipeline for estimating the dimensionality of experimental neural data. Public Library of Science 2021-11-29 /pmc/articles/PMC8659648/ /pubmed/34843461 http://dx.doi.org/10.1371/journal.pcbi.1008591 Text en © 2021 Altan et al 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
Altan, Ege
Solla, Sara A.
Miller, Lee E.
Perreault, Eric J.
Estimating the dimensionality of the manifold underlying multi-electrode neural recordings
title Estimating the dimensionality of the manifold underlying multi-electrode neural recordings
title_full Estimating the dimensionality of the manifold underlying multi-electrode neural recordings
title_fullStr Estimating the dimensionality of the manifold underlying multi-electrode neural recordings
title_full_unstemmed Estimating the dimensionality of the manifold underlying multi-electrode neural recordings
title_short Estimating the dimensionality of the manifold underlying multi-electrode neural recordings
title_sort estimating the dimensionality of the manifold underlying multi-electrode neural recordings
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8659648/
https://www.ncbi.nlm.nih.gov/pubmed/34843461
http://dx.doi.org/10.1371/journal.pcbi.1008591
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