A Quality Measure for Repeating Multiple-Unit Spike Patterns

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Abstract We propose a quality measure for spatio-temporal spike patterns (STPs) in multiple-neuron recordings. In such recordings, repeating STPs or pattern repetitions (PRs) are often found, with many of these generated by chance. To rule those out, statistical tests have been developed to discriminate the unlikely from the more likely PRs. This statistical problem is complicated by the fact that there are several obvious quality criteria for a PR, such as the size (the number of spikes) of the pattern and the number of its occurrences. Here, we propose a canonical way of combining several criteria (which we collect in the so-called signature of the pattern) into a single quality measure, based on the ‘unlikeliness’ of the pattern. This measure is defined mathematically, and a formula for its computation is derived for stationary spike trains. It can be used to compare PRs. Since spike trains are not stationary in practice, we discuss, for two experimental data sets, how well the stationary formula correlates with the defined quality measure as determined from simulations. The results encourage the use of the stationary formula or also some simpler, related formulas as ‘proxies’ for the quality, for the comparison of PRs and also for statistical tests that avoid the multiple testing problem incurred by using several quality criteria. Based on our results, we propose a few test statistics, i.e., random variables on the space of multi-unit spike trains with an appropriate null-hypothesis distribution, to evaluate STPs with less computational and sampling efforts.

Authors 5

1. Günther Palm

   Forschungszentrum Jülich · Universität Ulm

   Affiliation as printed

   Institute for Advanced Simulation (IAS-6), Forschungszentrum Jülich, Jülich, Germany

   Neuroinformatics Department, University of Ulm, Ulm, Germany

2. Monica Paoletti

   Scuola Internazionale Superiore di Studi Avanzati · Forschungszentrum Jülich

   Affiliation as printed

   Cognitive Neuroscience Department, Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, Italy

   Institute for Advanced Simulation (IAS-6), Forschungszentrum Jülich, Jülich, Germany

3. Junji Ito corresponding

   Forschungszentrum Jülich

   Affiliation as printed

   Institute for Advanced Simulation (IAS-6), Forschungszentrum Jülich, Jülich, Germany

4. Alessandra Stella

   Forschungszentrum Jülich

   Affiliation as printed

   Institute for Advanced Simulation (IAS-6), Forschungszentrum Jülich, Jülich, Germany

5. Sonja Grün Aachen Theoretical Systems Neurobiology

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

   Institute for Advanced Simulation (IAS-6), Forschungszentrum Jülich, Jülich, Germany

   JARA-Institute Brain Structure-Function Relationships (INM-10), Forschungszentrum Jülich, Wilhelm-Johnen-Str., Jülich, 52428, Germany

   Theoretical Systems neurobiology, RWTH Aachen University, Aachen, Germany