A metric space approach to the information capacity of spike trains.
James Gillespie and Conor Houghton,
The Journal of Computational Neuroscience, 30 (2011) 201-209.

Abstract A novel method is presented for calculating the information channel capacity of spike trains. This method works by fitting a chi-distribution to the distribution of distances between responses to the same stimulus: the chi-distribution is the length distribution for a vector of Gaussian variables. The dimension of this vector defines an effective dimension for the noise and by rephrasing the problem in terms of distance based quantities, this allows the channel capacity to be cal culated. As an example, the capacity is calculated for a data set recorded from auditory neurons in zebra finch.

Blurb: A novel effective noise dimension is defined for spike train data; this rephases the problem of information transmission by spike trains into a form that allows channel capacity to be calculating, giving a straight-forward measure of the information carry capacity for neuron to neuron signalling.