Calculating the Mutual Information between Two Spike Trains

Conor Houghton

doi:10.1162/neco_a_01155

Calculating the Mutual Information between Two Spike Trains

Conor Houghton^*

^*Corresponding author for this work

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9 Citations (Scopus)

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Abstract

It is difficult to estimate the mutual information between spike trains because established methods require more data than are usually available. Kozachenko-Leonenko estimators promise to solve this problem but include a smoothing parameter that must be set. We propose here that the smoothing parameter can be selected by maximizing the estimated unbiased mutual information. This is tested on fictive data and shown to work very well.

Original language	English
Pages (from-to)	330-343
Number of pages	14
Journal	Neural Computation
Volume	31
Issue number	2
Early online date	18 Jan 2019
DOIs	https://doi.org/10.1162/neco_a_01155 https://doi.org/10.1162/neco_a_01155
Publication status	Published - 1 Feb 2019

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AB - It is difficult to estimate the mutual information between spike trains because established methods require more data than are usually available. Kozachenko-Leonenko estimators promise to solve this problem but include a smoothing parameter that must be set. We propose here that the smoothing parameter can be selected by maximizing the estimated unbiased mutual information. This is tested on fictive data and shown to work very well.

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Calculating the Mutual Information between Two Spike Trains

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