Volume 8 Supplement 2

Sixteenth Annual Computational Neuroscience Meeting: CNS*2007

Open Access

Measuring spike train synchrony and reliability

  • Thomas Kreuz1Email author,
  • Julie S Haas2,
  • Alice Morelli3,
  • Henry DI Abarbanel2, 4 and
  • Antonio Politi1
BMC Neuroscience20078(Suppl 2):P79

DOI: 10.1186/1471-2202-8-S2-P79

Published: 6 July 2007

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 fixed beforehand. In this study [1], we propose the ISI-distance, a simple complementary approach that extracts information from the interspike intervals by evaluating the ratio of the instantaneous frequencies. 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 (cf. [2]). We compare the method with six existing approaches (two spike train metrics [3, 4], a correlation measure [2, 5], a similarity measure [6], and event synchronization [7]) using spike trains extracted from a simulated Hindemarsh-Rose network [8]. In this comparison the ISI-distance performs as well as the best time-scale-optimized measure based on spike timing, without requiring an externally determined time scale for interaction or comparison.



TK has been supported by the Marie Curie Individual Intra-European Fellowship "DEAN", project No 011434. JSH acknowledges financial support by the San Diego Foundation.

Authors’ Affiliations

Istituto dei Sistemi Complessi – CNR
Institute for Nonlinear Sciences, University of California
Istituto Nazionale di Ottica Applicata
Department of Physics and Marine Physical Laboratory (Scripps Institution of Oceanography), University of California


  1. Kreuz T, Haas JS, Morelli A, Abarbanel HDI, Politi A: Measuring spike train synchronization. [http://arxiv.org/abs/physics/0701261]
  2. Haas JS, White JA: Frequency selectivity of layer II stellate cells in the medial entorhinal cortex. J Neurophysiol. 2002, 88: 2422-2429. 10.1152/jn.00598.2002.PubMedView ArticleGoogle Scholar
  3. Victor J, Purpura K: Nature and precision of temporal coding in visual cortex: A metric-space analysis. J Neurophysiol. 1996, 76: 1310-PubMedGoogle Scholar
  4. van Rossum MCW: A novel spike distance. Neural Computation. 2001, 13: 751-10.1162/089976601300014321.PubMedView ArticleGoogle Scholar
  5. Schreiber S, Fellous JM, Whitmer JH, Tiesinga PHE, Sejnowski TJ: A new correlation-based measure of spike timing reliability. Neurocomputing. 2003, 52: 925-PubMedView ArticleGoogle Scholar
  6. Hunter JD, Milton G: Amplitude and frequency dependence of spike timing: implications for dynamic regulation. J Neurophysiol. 2003, 90: 387-10.1152/jn.00074.2003.PubMedView ArticleGoogle Scholar
  7. Quian Quiroga R, Kreuz T, Grassberger P: Event synchronization: A simple and fast method to measure synchronicity and time delay patterns. Phys Rev E. 2002, 66: 041904-10.1103/PhysRevE.66.041904.View ArticleGoogle Scholar
  8. Morelli A, Grotto RL, Arecchi FT: Neural coding for the retrieval of multiple memory patterns. Biosystems. 2006, 86: 100-10.1016/j.biosystems.2006.03.011.PubMedView ArticleGoogle Scholar


© Kreuz et al; licensee BioMed Central Ltd. 2007

This article is published under license to BioMed Central Ltd.