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

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**Citation: **

*James Gillespie and Conor Houghton, A metric space approach to the information capacity of spike trains, 2010*

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A metric space approach to the information capacity of spike trains.pdf (Pre-print (author's copy) - Non-Peer Reviewed) 376.3Kb

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**Abstract: **

Classical information theory can be either discrete or continuous, corresponding to discrete or continuous random variables. However, although spike
times in a spike train are described by continuous variables, the information content is usually calculated using discrete information theory. This is because
the number of spikes, and hence, the number of variables, varies from spike train to spike train, making the continuous theory difficult to apply.It is possible to avoid this problem by using a metric space approach to spike trains. A metric gives a distance between different spike trains. The
continuous version of information theory is then rephrased in terms of metric quantities and used to estimate the information capacity of spike trains.
This method works by matching the distribution of distances between responses to the same stimulus to a -distribution: the -distribution is the length
distribution for a vector of Gaussian variables. This defines a noise dimension for the spike train and gives a bound on the channel capacity.

**Sponsor**

**Grant Number**

Science Foundation Ireland

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**Author's Homepage: **

http://people.tcd.ie/houghtcj
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**Description: **

PUBLISHED
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**Type of material: **

Working Paper#####
**Availability: **

Full text available#####
**Keywords: **

Mathematics, spike trains
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