Estimating mutual information for spike trains: a bird song example

TL;DR

This paper demonstrates how to estimate mutual information between stimuli and neural spike trains in zebra finches using a coordinate-free Kozachenko-Leonenko estimator, revealing consistent information content during song progression.

Contribution

It introduces the application of a coordinate-free mutual information estimator to neural spike train data in a bird song context, highlighting its effectiveness.

Findings

Mutual information remains stable throughout the song.

The Kozachenko-Leonenko estimator is effective for spike train data.

No decline in information content as the song progresses.

Abstract

Zebra finch are a model animal used in the study of audition. They are adept at recognizing zebra finch songs and the neural pathway involved in song recognition is well studied. Here, this example is used to illustrate the estimation of mutual information between stimulus and response using a Kozachenko-Leonenko estimator. The challenge in calculating mutual information for spike trains is that there are no obvious coordinates for the data. The Kozachenko-Leonenko estimator does not require coordinates, it relies only on the distance between data points. In the case of bird song, estimating the mutual information demonstrates that the information content of spiking does not diminish as the song progresses.