Literature DB >> 17628690

Measuring spike train synchrony.

Thomas Kreuz1, Julie S Haas, Alice Morelli, Henry D I Abarbanel, Antonio Politi.   

Abstract

Estimating the degree of synchrony or reliability between two or more spike trains is a frequent task in both experimental and computational neuroscience. In recent years, many different methods have been proposed that typically compare the timing of spikes on a certain time scale to be optimized by the analyst. Here, we propose the ISI-distance, a simple complementary approach that extracts information from the interspike intervals by evaluating the ratio of the instantaneous firing rates. The method is parameter free, time scale independent and easy to visualize as illustrated by an application to real neuronal spike trains obtained in vitro from rat slices. In a comparison with existing approaches on spike trains extracted from a simulated Hindemarsh-Rose network, the ISI-distance performs as well as the best time-scale-optimized measure based on spike timing.

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Year:  2007        PMID: 17628690     DOI: 10.1016/j.jneumeth.2007.05.031

Source DB:  PubMed          Journal:  J Neurosci Methods        ISSN: 0165-0270            Impact factor:   2.390


  44 in total

1.  Neural coding properties based on spike timing and pattern correlation of retinal ganglion cells.

Authors:  Han-Yan Gong; Ying-Ying Zhang; Pei-Ji Liang; Pu-Ming Zhang
Journal:  Cogn Neurodyn       Date:  2010-06-29       Impact factor: 5.082

2.  Scale-free topology of the CA3 hippocampal network: a novel method to analyze functional neuronal assemblies.

Authors:  Xiaoli Li; Gaoxiang Ouyang; Astushi Usami; Yuji Ikegaya; Attila Sik
Journal:  Biophys J       Date:  2010-05-19       Impact factor: 4.033

3.  Quantification of clustering in joint interspike interval scattergrams of spike trains.

Authors:  Ramana Dodla; Charles J Wilson
Journal:  Biophys J       Date:  2010-06-02       Impact factor: 4.033

4.  A new multineuron spike train metric.

Authors:  Conor Houghton; Kamal Sen
Journal:  Neural Comput       Date:  2008-06       Impact factor: 2.026

5.  Reliability of spike and burst firing in thalamocortical relay cells.

Authors:  Fleur Zeldenrust; Pascal J P Chameau; Wytse J Wadman
Journal:  J Comput Neurosci       Date:  2013-05-25       Impact factor: 1.621

6.  A phase function to quantify serial dependence between discrete samples.

Authors:  Ramana Dodla; Charles J Wilson
Journal:  Biophys J       Date:  2010-02-17       Impact factor: 4.033

7.  Using interspike intervals to quantify noise effects on spike trains in temperature encoding neurons.

Authors:  Ying Du; Qishao Lu; Rubin Wang
Journal:  Cogn Neurodyn       Date:  2010-04-27       Impact factor: 5.082

8.  An information-geometric framework for statistical inferences in the neural spike train space.

Authors:  Wei Wu; Anuj Srivastava
Journal:  J Comput Neurosci       Date:  2011-05-17       Impact factor: 1.621

9.  Functional clustering algorithm for the analysis of dynamic network data.

Authors:  S Feldt; J Waddell; V L Hetrick; J D Berke; M Zochowski
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2009-05-07

10.  Consistency and diversity of spike dynamics in the neurons of bed nucleus of stria terminalis of the rat: a dynamic clamp study.

Authors:  Attila Szücs; Fulvia Berton; Thomas Nowotny; Pietro Sanna; Walter Francesconi
Journal:  PLoS One       Date:  2010-08-03       Impact factor: 3.240

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