Journal Article

FZJ-2013-03923

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Impact of Spike Train Autostructure on Probability Distribution of Joint Spike Events

Pipa, G. (Corresponding author) ; Grün, S.FZJ* ; van Vreeswijk, C.

2013
MIT Press Cambridge, Mass.

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Please use a persistent id in citations: http://hdl.handle.net/2128/5358  doi:10.1162/NECO_a_00432

Abstract: The discussion whether temporally coordinated spiking activity really exists and whether it is relevant has been heated over the past few years. To investigate this issue, several approaches have been taken to determine whether synchronized events occur significantly above chance, that is, whether they occur more often than expected if the neurons fire independently. Most investigations ignore or destroy the autostructure of the spiking activity of individual cells or assume Poissonian spiking as a model. Such methods that ignore the autostructure can significantly bias the coincidence statistics. Here, we study the influence of the autostructure on the probability distribution of coincident spiking events between tuples of mutually independent non-Poisson renewal processes. In particular, we consider two types of renewal processes that were suggested as appropriate models of experimental spike trains: a gamma and a log-normal process. For a gamma process, we characterize the shape of the distribution analytically with the Fano factor (FFc). In addition, we perform Monte Carlo estimations to derive the full shape of the distribution and the probability for false positives if a different process type is assumed as was actually present. We also determine how manipulations of such spike trains, here dithering, used for the generation of surrogate data change the distribution of coincident events and influence the significance estimation. We find, first, that the width of the coincidence count distribution and its FFc depend critically and in a nontrivial way on the detailed properties of the structure of the spike trains as characterized by the coefficient of variation CV. Second, the dependence of the FFc on the CV is complex and mostly nonmonotonic. Third, spike dithering, even if as small as a fraction of the interspike interval, can falsify the inference on coordinated firing.

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Contributing Institute(s):

1. Computational and Systems Neuroscience (INM-6)
2. Theoretical Neuroscience (IAS-6)

Research Program(s):

1. 331 - Signalling Pathways and Mechanisms in the Nervous System (POF2-331) (POF2-331)
2. HASB - Helmholtz Alliance on Systems Biology (HGF-SystemsBiology) (HGF-SystemsBiology)
3. BRAINSCALES - Brain-inspired multiscale computation in neuromorphic hybrid systems (269921) (269921)

Appears in the scientific report 2013

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Database coverage:
; ; Current Contents - Life Sciences ; JCR ; SCOPUS ; Science Citation Index ; Science Citation Index Expanded ; Thomson Reuters Master Journal List ; Web of Science Core Collection ; Zoological Record

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