Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/100917
Title: Quantifying statistical interdependence, part III : N > 2 point processes
Authors: Dauwels, Justin
Weber, Theophane
Vialatte, François-Benoît
Musha, Toshimitsu
Cichocki, Andrzej
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2011
Source: Dauwels, J., Weber, T., Vialatte, F., Musha, T., & Cichocki, A. (2012). Quantifying Statistical Interdependence, Part III: N > 2 Point Processes. Neural Computation, 24(2), 408-454.
Series/Report no.: Neural computation
Abstract: Stochastic event synchrony (SES) is a recently proposed family of similarity measures. First, “events” are extracted from the given signals; next, one tries to align events across the different time series. The better the alignment, the more similar the N time series are considered to be. The similarity measures quantify the reliability of the events (the fraction of “nonaligned” events) and the timing precision. So far, SES has been developed for pairs of one-dimensional (Part I) and multidimensional (Part II) point processes. In this letter (Part III), SES is extended from pairs of signals to N > 2 signals. The alignment and SES parameters are again determined through statistical inference, more specifically, by alternating two steps: (1) estimating the SES parameters from a given alignment and (2), with the resulting estimates, refining the alignment. The SES parameters are computed by maximum a posteriori (MAP) estimation (step 1), in analogy to the pairwise case. The alignment (step 2) is solved by linear integer programming. In order to test the robustness and reliability of the proposed N-variate SES method, it is first applied to synthetic data. We show that N-variate SES results in more reliable estimates than bivariate SES. Next N-variate SES is applied to two problems in neuroscience: to quantify the firing reliability of Morris-Lecar neurons and to detect anomalies in EEG synchrony of patients with mild cognitive impairment. Those problems were also considered in Parts I and II, respectively. In both cases, the N-variate SES approach yields a more detailed analysis.
URI: https://hdl.handle.net/10356/100917
http://hdl.handle.net/10220/11047
ISSN: 0899-7667
DOI: 10.1162/NECO_a_00235
Rights: © 2011 Massachusetts Institute of Technology. This paper was published in Neural Computation and is made available as an electronic reprint (preprint) with permission of Massachusetts Institute of Technology. The paper can be found at the following official DOI: [http://dx.doi.org/10.1162/NECO_a_00235]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

Files in This Item:
File Description SizeFormat 
Quantifying statistical interdependence, Part III N 2 point processes.pdf1.48 MBAdobe PDFThumbnail
View/Open

SCOPUSTM   
Citations 20

5
Updated on Sep 6, 2020

PublonsTM
Citations 20

3
Updated on Mar 6, 2021

Page view(s) 10

646
Updated on Jun 25, 2022

Download(s) 20

263
Updated on Jun 25, 2022

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.