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Artículo

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

Baglietto, GabrielIcon ; Gigante, Guido; Del Giudice, Paolo
Fecha de publicación: 04/2017
Editorial: Public Library of Science
Revista: Plos One
e-ISSN: 1932-6203
Idioma: Inglés
Tipo de recurso: Artículo publicado
Clasificación temática:
Otras Ciencias Físicas

Resumen

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.
Palabras clave: Neuroscience , Dimensional Reduction , Inference , Complexity Analysis
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info:eu-repo/semantics/openAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Unported (CC BY-NC-SA 2.5)
Identificadores
URI: http://hdl.handle.net/11336/47939
DOI: https://dx.doi.org/10.1371/journal.pone.0174918
URL: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0174918
Colecciones
Articulos(IFLYSIB)
Articulos de INST.FISICA DE LIQUIDOS Y SIST.BIOLOGICOS (I)
Citación
Baglietto, Gabriel; Gigante, Guido; Del Giudice, Paolo; Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis; Public Library of Science; Plos One; 12; 4; 4-2017; 1-25; e017491
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