Calculating the mutual information between two spike trains
Conor Houghton,
Neural Computation (2019) 31:330-343

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.

Blurb: This paper introduces a straight forward way to measure mutual information between spike trains; a problem which until now required more data that it is typically practical to record.