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This article is part of the supplement: Eighteenth Annual Computational Neuroscience Meeting: CNS*2009

Open Access Poster presentation

Auto-structure of spike trains matters for testing on synchronous activity

Gordon Pipa12*, Carl van Vreeswijk3 and Sonja Grun4

Author Affiliations

1 Max-Planck Institute for Brain Research and Frankfurt Institute for Advanced Studies, Germany

2 Massachusetts Institute of Technology and Massachusetts General Hospital, USA

3 Rene Descartes Univeristy, Laboratoire de Neurophysique et Physiologie, CNRS, France

4 Computational Neuroscience Group, RIKEN Brain Science Institute, Japan

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BMC Neuroscience 2009, 10(Suppl 1):P239  doi:10.1186/1471-2202-10-S1-P239


The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2202/10/S1/P239


Published:13 July 2009

© 2009 Pipa et al; licensee BioMed Central Ltd.

Poster presentation

Coordinated neuronal activity across many neurons, i.e. synchronous or spatiotemporal pattern, had been believed to be a major component of neuronal activity. However, the discussion if coordinated activity really exists remained heated and controversial. A major uncertainty was that many analysis approaches either ignored the auto-structure of the spiking activity, assumed a very simplified model (poissonian firing), or changed the auto-structure by spike jittering. We studied whether a statistical inference that tests whether coordinated activity is occurring beyond chance can be made false if one ignores or changes the real auto-structure of recorded data. To this end, we investigated the distribution of coincident spikes in mutually independent spike-trains modeled as renewal processes. We considered Gamma processes with different shape parameters as well as renewal processes in which the ISI distribution is log-normal. For Gamma processes of integer order, we calculated the mean number of coincident spikes, as well as the Fano factor of the coincidences, analytically. We determined how these measures depend on the bin width and also investigated how they depend on the firing rate, and on rate difference between the neurons. We used Monte-Carlo simulations to estimate the whole distribution for these parameters and also for other values of gamma. Moreover, we considered the effect of dithering for both of these processes and saw that while dithering does not change the average number of coincidences, it does change the shape of the coincidence distribution.

Our major findings are: 1) the width of the coincidence count distribution depends very critically and in a non-trivial way on the detailed properties of the inter-spike interval distribution, 2) the dependencies of the Fano factor on the coefficient of variation of the ISI distribution are complex and mostly non-monotonic. Moreover, the Fano factor depends on the very detailed properties of the individual point processes, and cannot be predicted by the CV alone. Hence, given a recorded data set, the estimated value of CV of the ISI distribution is not sufficient to predict the Fano factor of the coincidence count distribution, and 3) spike jittering, even if it is as small as a fraction of the expected ISI, can falsify the inference on coordinated firing. In most of the tested cases and especially for complex synchronous and spatiotemporal pattern across many neurons, spike jittering increased the likelihood of false positive finding very strongly. Last, we discuss a procedure [1] that considers the complete auto-structure of each individual spike-train for testing whether synchrony firing occurs at chance and therefore overcomes the danger of an increased level of false positives.

Acknowledgements

Supported by the Hertie Foundation, the EU (GABA project FP6-2005-NEST-Path-043309), the German Ministry for Education and Research (BMBF grants 01GQ01413), and the Stifterverband fur die Deutsche Wissenschaft.

References

  1. Pipa G, Wheeler DW, Singer W, Nikolic D: NeuroXidence: Reliable and efficient analysis of an excess or deficiency of joint-spike events.

    J Comp Neuroscience 2008, 25:64-88. Publisher Full Text OpenURL