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This article is part of the supplement: Sixteenth Annual Computational Neuroscience Meeting: CNS*2007

Open Access Poster presentation

Measuring spike train synchrony and reliability

Thomas Kreuz1*, Julie S Haas2, Alice Morelli3, Henry DI Abarbanel24 and Antonio Politi1

Author Affiliations

1 Istituto dei Sistemi Complessi – CNR, Sesto Fiorentino, Italy

2 Institute for Nonlinear Sciences, University of California, San Diego, CA, USA

3 Istituto Nazionale di Ottica Applicata, Firenze, Italy

4 Department of Physics and Marine Physical Laboratory (Scripps Institution of Oceanography), University of California, San Diego, CA, USA

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BMC Neuroscience 2007, 8(Suppl 2):P79  doi:10.1186/1471-2202-8-S2-P79


The electronic version of this article is the complete one and can be found online at:


Published:6 July 2007

© 2007 Kreuz et al; licensee BioMed Central Ltd.

Poster presentation

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.

Acknowledgements

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.

References

  1. Kreuz T, Haas JS, Morelli A, Abarbanel HDI, Politi A: Measuring spike train synchronization. [http://arxiv.org/abs/physics/0701261] webcite

  2. Haas JS, White JA: Frequency selectivity of layer II stellate cells in the medial entorhinal cortex.

    J Neurophysiol 2002, 88:2422-2429. PubMed Abstract | Publisher Full Text OpenURL

  3. Victor J, Purpura K: Nature and precision of temporal coding in visual cortex: A metric-space analysis.

    J Neurophysiol 1996, 76:1310. PubMed Abstract | Publisher Full Text OpenURL

  4. van Rossum MCW: A novel spike distance.

    Neural Computation 2001, 13:751. PubMed Abstract | Publisher Full Text OpenURL

  5. Schreiber S, Fellous JM, Whitmer JH, Tiesinga PHE, Sejnowski TJ: A new correlation-based measure of spike timing reliability.

    Neurocomputing 2003, 52:925. OpenURL

  6. Hunter JD, Milton G: Amplitude and frequency dependence of spike timing: implications for dynamic regulation.

    J Neurophysiol 2003, 90:387. PubMed Abstract | Publisher Full Text OpenURL

  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. Publisher Full Text OpenURL

  8. Morelli A, Grotto RL, Arecchi FT: Neural coding for the retrieval of multiple memory patterns.

    Biosystems 2006, 86:100. PubMed Abstract | Publisher Full Text OpenURL