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An O(n) method of calculating Kendall correlations of spike trains

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  • William Redman

Abstract

The ability to record from increasingly large numbers of neurons, and the increasing attention being paid to large scale neural network simulations, demands computationally fast algorithms to compute relevant statistical measures. We present an O(n) algorithm for calculating the Kendall correlation of spike trains, a correlation measure that is becoming especially recognized as an important tool in neuroscience. We show that our method is around 50 times faster than the O (n ln n) method which is a current standard for quickly computing the Kendall correlation. In addition to providing a faster algorithm, we emphasize the role that taking the specific nature of spike trains had on reducing the run time. We imagine that there are many other useful algorithms that can be even more significantly sped up when taking this into consideration. A MATLAB function executing the method described here has been made freely available on-line.

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  • William Redman, 2019. "An O(n) method of calculating Kendall correlations of spike trains," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-7, February.
    • Handle: RePEc:plo:pone00:0212190
    DOI: 10.1371/journal.pone.0212190
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    Cited by:

    1. Francisco Pedroche & J. Alberto Conejero, 2020. "Corrected Evolutive Kendall’s τ Coefficients for Incomplete Rankings with Ties: Application to Case of Spotify Lists," Mathematics, MDPI, vol. 8(10), pages 1-30, October.

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