Despite the fact that the Kendall correlation is becoming recognized as an important tool in neuroscience, most standard algorithms to compute the Kendall correlation of large spike trains are computationally very slow. Here we present a new, significantly faster method for calculating the Kendall correlation of spike trains that takes advantage of their structure . We show that our method is more than 300 times faster than traditional approaches for particularly large, sparse spike trains, and still more than 10 times faster for large, but considerably less sparse, spike trains . A MATLAB function executing the method described here was made freely available on-line.
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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Despite the fact that the Kendall correlation is becoming recognized as an important tool in neuroscience, most standard
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
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the Kendall correlation of large spike trains are computationally very slow. Here we present a new, significantly faster method
relevant statistical measures. We present an O(n) algorithm
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that takes advantage of their structure
, a correlation measure that is becoming especially recognized as an important tool in neuroscience
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more than 300
around 50
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traditional approaches for particularly large, sparse spike trains, and still more than 10 times faster for large, but considerably less sparse, spike trains .
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.
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was
has been