Original Research ARTICLE
Statistical evaluation of synchronous spike patterns extracted by frequent item set mining
- 1Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6), Jülich Research Centre and JARA, Jülich, Germany
- 2European Centre for Soft Computing, Mieres, Spain
- 3Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany
We recently proposed frequent itemset mining (FIM) as a method to perform an optimized search for patterns of synchronous spikes (item sets) in massively parallel spike trains. This search outputs the occurrence count (support) of individual patterns that are not trivially explained by the counts of any superset (closed frequent item sets). The number of patterns found by FIM makes direct statistical tests infeasible due to severe multiple testing. To overcome this issue, we proposed to test the significance not of individual patterns, but instead of their signatures, defined as the pairs of pattern size z and support c. Here, we derive in detail a statistical test for the significance of the signatures under the null hypothesis of full independence (pattern spectrum filtering, PSF) by means of surrogate data. As a result, injected spike patterns that mimic assembly activity are well detected, yielding a low false negative rate. However, this approach is prone to additionally classify patterns resulting from chance overlap of real assembly activity and background spiking as significant. These patterns represent false positives with respect to the null hypothesis of having one assembly of given signature embedded in otherwise independent spiking activity. We propose the additional method of pattern set reduction (PSR) to remove these false positives by conditional filtering. By employing stochastic simulations of parallel spike trains with correlated activity in form of injected spike synchrony in subsets of the neurons, we demonstrate for a range of parameter settings that the analysis scheme composed of FIM, PSF and PSR allows to reliably detect active assemblies in massively parallel spike trains.
Keywords: higher-order correlations, neuronal cell assemblies, spike patterns, spike synchrony, multiple testing, data mining
Citation: Torre E, Picado-Muiño D, Denker M, Borgelt C and Grün S (2013) Statistical evaluation of synchronous spike patterns extracted by frequent item set mining. Front. Comput. Neurosci. 7:132. doi: 10.3389/fncom.2013.00132
Received: 31 May 2013; Accepted: 11 September 2013;
Published online: 23 October 2013.
Edited by:Ruben Moreno-Bote, Foundation Sant Joan de Deu, Spain
Reviewed by:Shigeru Shinomoto, Kyoto University, Japan
Srdjan Ostojic, Ecole Normale Superieure, France
Copyright © 2013 Torre, Picado-Muiño, Denker, Borgelt and Grün. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Emiliano Torre, Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6), Jülich Research Centre, Wilhelm-Johnen-Strasse, 52425 Jülich, Germany e-mail: email@example.com