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Computational Intelligence and Neuroscience
Volume 2015 (2015), Article ID 427829, 12 pages
Research Article

Test Statistics for the Identification of Assembly Neurons in Parallel Spike Trains

European Centre for Soft Computing, Edificio Científico Tecnológico, Gonzalo Gutiérrez Quirós, s/n, 33600 Mieres, Spain

Received 13 September 2014; Revised 13 February 2015; Accepted 18 February 2015

Academic Editor: Jianwei Shuai

Copyright © 2015 David Picado Muiño and Christian Borgelt. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In recent years numerous improvements have been made in multiple-electrode recordings (i.e., parallel spike-train recordings) and spike sorting to the extent that nowadays it is possible to monitor the activity of up to hundreds of neurons simultaneously. Due to these improvements it is now potentially possible to identify assembly activity (roughly understood as significant synchronous spiking of a group of neurons) from these recordings, which—if it can be demonstrated reliably—would significantly improve our understanding of neural activity and neural coding. However, several methodological problems remain when trying to do so and, among them, a principal one is the combinatorial explosion that one faces when considering all potential neuronal assemblies, since in principle every subset of the recorded neurons constitutes a candidate set for an assembly. We present several statistical tests to identify assembly neurons (i.e., neurons that participate in a neuronal assembly) from parallel spike trains with the aim of reducing the set of neurons to a relevant subset of them and this way ease the task of identifying neuronal assemblies in further analyses. These tests are an improvement of those introduced in the work by Berger et al. (2010) based on additional features like spike weight or pairwise overlap and on alternative ways to identify spike coincidences (e.g., by avoiding time binning, which tends to lose information).