Advertisement

BMC Neuroscience

, 8:P79 | Cite as

Measuring spike train synchrony and reliability

  • Thomas Kreuz
  • Julie S Haas
  • Alice Morelli
  • Henry DI Abarbanel
  • Antonio Politi
Open Access
Poster presentation

Keywords

Animal Model Similarity Measure Spike Train Instantaneous Frequency Complementary Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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.

Notes

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. 1.
    Kreuz T, Haas JS, Morelli A, Abarbanel HDI, Politi A: Measuring spike train synchronization. [http://arxiv.org/abs/physics/0701261]
  2. 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.PubMedCrossRefGoogle Scholar
  3. 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. 4.
    van Rossum MCW: A novel spike distance. Neural Computation. 2001, 13: 751-10.1162/089976601300014321.PubMedCrossRefGoogle Scholar
  5. 5.
    Schreiber S, Fellous JM, Whitmer JH, Tiesinga PHE, Sejnowski TJ: A new correlation-based measure of spike timing reliability. Neurocomputing. 2003, 52: 925-PubMedCrossRefGoogle Scholar
  6. 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.PubMedCrossRefGoogle Scholar
  7. 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.CrossRefGoogle Scholar
  8. 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.PubMedCrossRefGoogle Scholar

Copyright information

© Kreuz et al; licensee BioMed Central Ltd. 2007

This article is published under license to BioMed Central Ltd.

Authors and Affiliations

  • Thomas Kreuz
    • 1
  • Julie S Haas
    • 2
  • Alice Morelli
    • 3
  • Henry DI Abarbanel
    • 2
    • 4
  • Antonio Politi
    • 1
  1. 1.Istituto dei Sistemi Complessi – CNRSesto FiorentinoItaly
  2. 2.Institute for Nonlinear SciencesUniversity of CaliforniaSan DiegoUSA
  3. 3.Istituto Nazionale di Ottica ApplicataFirenzeItaly
  4. 4.Department of Physics and Marine Physical Laboratory (Scripps Institution of Oceanography)University of CaliforniaSan DiegoUSA

Personalised recommendations