Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Marsat, G., Daly, K.C., Drew, J.A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
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
_version_ 1785060349791174656
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
work_keys_str_mv AT marsatg characterizingneuralcodingperformanceforpopulationsofsensoryneuronscomparingaweightedspikedistancemetricstootheranalyticalmethods
AT dalykc characterizingneuralcodingperformanceforpopulationsofsensoryneuronscomparingaweightedspikedistancemetricstootheranalyticalmethods
AT drewja characterizingneuralcodingperformanceforpopulationsofsensoryneuronscomparingaweightedspikedistancemetricstootheranalyticalmethods