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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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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 |
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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 |
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