Cargando…

Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis

Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underl...

Descripción completa

Detalles Bibliográficos
Autores principales: Baglietto, Gabriel, Gigante, Guido, Del Giudice, Paolo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5378378/
https://www.ncbi.nlm.nih.gov/pubmed/28369106
http://dx.doi.org/10.1371/journal.pone.0174918
_version_ 1782519434189996032
author Baglietto, Gabriel
Gigante, Guido
Del Giudice, Paolo
author_facet Baglietto, Gabriel
Gigante, Guido
Del Giudice, Paolo
author_sort Baglietto, Gabriel
collection PubMed
description Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underlying neural network from the recorded neural activities. In the present paper we show that state-space clustering can provide an easy and effective option for reducing the dimensionality of multiple neural time series, that it can improve inference of synaptic couplings from neural activities, and that it can also allow the construction of a compact representation of the multi-dimensional dynamics, that easily lends itself to complexity measures. We apply a variant of the ‘mean-shift’ algorithm to perform state-space clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memories embedded in the synaptic matrix. In this context, we show that the neural states identified as clusters’ centroids offer a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities. Moving to the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects, we propose a procedure inspired by Boltzmann learning, but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations; we then illustrate, in the spiking network, how clustering is effective in extracting relevant features of the network’s state-space landscape. Finally, we show that the knowledge of the cluster structure allows casting the multi-dimensional neural dynamics in the form of a symbolic dynamics of transitions between clusters; as an illustration of the potential of such reduction, we define and analyze a measure of complexity of the neural time series.
format Online
Article
Text
id pubmed-5378378
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-53783782017-04-07 Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis Baglietto, Gabriel Gigante, Guido Del Giudice, Paolo PLoS One Research Article Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underlying neural network from the recorded neural activities. In the present paper we show that state-space clustering can provide an easy and effective option for reducing the dimensionality of multiple neural time series, that it can improve inference of synaptic couplings from neural activities, and that it can also allow the construction of a compact representation of the multi-dimensional dynamics, that easily lends itself to complexity measures. We apply a variant of the ‘mean-shift’ algorithm to perform state-space clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memories embedded in the synaptic matrix. In this context, we show that the neural states identified as clusters’ centroids offer a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities. Moving to the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects, we propose a procedure inspired by Boltzmann learning, but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations; we then illustrate, in the spiking network, how clustering is effective in extracting relevant features of the network’s state-space landscape. Finally, we show that the knowledge of the cluster structure allows casting the multi-dimensional neural dynamics in the form of a symbolic dynamics of transitions between clusters; as an illustration of the potential of such reduction, we define and analyze a measure of complexity of the neural time series. Public Library of Science 2017-04-03 /pmc/articles/PMC5378378/ /pubmed/28369106 http://dx.doi.org/10.1371/journal.pone.0174918 Text en © 2017 Baglietto et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://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
Baglietto, Gabriel
Gigante, Guido
Del Giudice, Paolo
Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis
title Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis
title_full Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis
title_fullStr Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis
title_full_unstemmed Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis
title_short Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis
title_sort density-based clustering: a ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5378378/
https://www.ncbi.nlm.nih.gov/pubmed/28369106
http://dx.doi.org/10.1371/journal.pone.0174918
work_keys_str_mv AT bagliettogabriel densitybasedclusteringalandscapeviewofmultichannelneuraldataforinferenceanddynamiccomplexityanalysis
AT giganteguido densitybasedclusteringalandscapeviewofmultichannelneuraldataforinferenceanddynamiccomplexityanalysis
AT delgiudicepaolo densitybasedclusteringalandscapeviewofmultichannelneuraldataforinferenceanddynamiccomplexityanalysis