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Characterizing neural coding performance for populations of sensory neurons: comparing a weighted spike distance metrics to other analytical methods
The identity of sensory stimuli is encoded in the spatio-temporal patterns of responses of the encoding neural population. For stimuli to be discriminated reliably, differences in population responses must be accurately decoded by downstream networks. Several methods to compare patterns of responses...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10277732/ https://www.ncbi.nlm.nih.gov/pubmed/37342463 http://dx.doi.org/10.3389/fnins.2023.1175629 |
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author | Marsat, G. Daly, K.C. Drew, J.A. |
author_facet | Marsat, G. Daly, K.C. Drew, J.A. |
author_sort | Marsat, G. |
collection | PubMed |
description | The identity of sensory stimuli is encoded in the spatio-temporal patterns of responses of the encoding neural population. For stimuli to be discriminated reliably, differences in population responses must be accurately decoded by downstream networks. Several methods to compare patterns of responses have been used by neurophysiologists to characterize the accuracy of the sensory responses studied. Among the most widely used analyses, we note methods based on Euclidean distances or on spike metric distances. Methods based on artificial neural networks and machine learning that recognize and/or classify specific input patterns have also gained popularity. Here, we first compare these three strategies using datasets from three different model systems: the moth olfactory system, the electrosensory system of gymnotids, and leaky-integrate-and-fire (LIF) model responses. We show that the input-weighting procedure inherent to artificial neural networks allows the efficient extraction of information relevant to stimulus discrimination. To combine the convenience of methods such as spike metric distances but leverage the advantages of weighting the inputs, we propose a measure based on geometric distances where each dimension is weighted proportionally to how informative it is. We show that the result of this Weighted Euclidian Distance (WED) analysis performs as well or better than the artificial neural network we tested and outperforms the more traditional spike distance metrics. We applied information theoretic analysis to LIF responses and compared their encoding accuracy with the discrimination accuracy quantified through this WED analysis. We show a high degree of correlation between discrimination accuracy and information content, and that our weighting procedure allowed the efficient use of information present to perform the discrimination task. We argue that our proposed measure provides the flexibility and ease of use sought by neurophysiologists while providing a more powerful way to extract relevant information than more traditional methods. |
format | Online Article Text |
id | pubmed-10277732 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102777322023-06-20 Characterizing neural coding performance for populations of sensory neurons: comparing a weighted spike distance metrics to other analytical methods Marsat, G. Daly, K.C. Drew, J.A. Front Neurosci Neuroscience The identity of sensory stimuli is encoded in the spatio-temporal patterns of responses of the encoding neural population. For stimuli to be discriminated reliably, differences in population responses must be accurately decoded by downstream networks. Several methods to compare patterns of responses have been used by neurophysiologists to characterize the accuracy of the sensory responses studied. Among the most widely used analyses, we note methods based on Euclidean distances or on spike metric distances. Methods based on artificial neural networks and machine learning that recognize and/or classify specific input patterns have also gained popularity. Here, we first compare these three strategies using datasets from three different model systems: the moth olfactory system, the electrosensory system of gymnotids, and leaky-integrate-and-fire (LIF) model responses. We show that the input-weighting procedure inherent to artificial neural networks allows the efficient extraction of information relevant to stimulus discrimination. To combine the convenience of methods such as spike metric distances but leverage the advantages of weighting the inputs, we propose a measure based on geometric distances where each dimension is weighted proportionally to how informative it is. We show that the result of this Weighted Euclidian Distance (WED) analysis performs as well or better than the artificial neural network we tested and outperforms the more traditional spike distance metrics. We applied information theoretic analysis to LIF responses and compared their encoding accuracy with the discrimination accuracy quantified through this WED analysis. We show a high degree of correlation between discrimination accuracy and information content, and that our weighting procedure allowed the efficient use of information present to perform the discrimination task. We argue that our proposed measure provides the flexibility and ease of use sought by neurophysiologists while providing a more powerful way to extract relevant information than more traditional methods. Frontiers Media S.A. 2023-06-05 /pmc/articles/PMC10277732/ /pubmed/37342463 http://dx.doi.org/10.3389/fnins.2023.1175629 Text en Copyright © 2023 Marsat, Daly and Drew. https://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 Marsat, G. Daly, K.C. Drew, J.A. Characterizing neural coding performance for populations of sensory neurons: comparing a weighted spike distance metrics to other analytical methods |
title | Characterizing neural coding performance for populations of sensory neurons: comparing a weighted spike distance metrics to other analytical methods |
title_full | Characterizing neural coding performance for populations of sensory neurons: comparing a weighted spike distance metrics to other analytical methods |
title_fullStr | Characterizing neural coding performance for populations of sensory neurons: comparing a weighted spike distance metrics to other analytical methods |
title_full_unstemmed | Characterizing neural coding performance for populations of sensory neurons: comparing a weighted spike distance metrics to other analytical methods |
title_short | Characterizing neural coding performance for populations of sensory neurons: comparing a weighted spike distance metrics to other analytical methods |
title_sort | characterizing neural coding performance for populations of sensory neurons: comparing a weighted spike distance metrics to other analytical methods |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10277732/ https://www.ncbi.nlm.nih.gov/pubmed/37342463 http://dx.doi.org/10.3389/fnins.2023.1175629 |
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