A metric space approach to the information channel capacity of spike trains

Authors:
James B. Gillespie;Conor J. Houghton
Affiliations:
School of Mathematics, Trinity College Dublin, Dublin 2, Ireland;School of Mathematics, Trinity College Dublin, Dublin 2, Ireland
Venue:
Journal of Computational Neuroscience
Year:
2011

Citing 3
Cited 1

Elements of information theory

Elements of information theory
Spikes: exploring the neural code

Spikes: exploring the neural code
A Novel Spike Distance

Neural Computation

Information theory in neuroscience

Journal of Computational Neuroscience

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Abstract

A novel method is presented for calculating the information channel capacity of spike trains. This method works by fitting a 驴-distribution to the distribution of distances between responses to the same stimulus: the 驴-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 calculated. As an example, the capacity is calculated for a data set recorded from auditory neurons in zebra finch.