Calculating the Mutual Information between Two Spike Trains

Conor Houghton*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

2 Citations (Scopus)
203 Downloads (Pure)


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 languageEnglish
Pages (from-to)330-343
Number of pages14
JournalNeural Computation
Issue number2
Early online date18 Jan 2019
Publication statusPublished - 1 Feb 2019

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