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dc.contributor.authorHoughton, Conor
dc.contributor.authorGillespie, James B.
dc.date.accessioned2011-07-25T08:39:13Z
dc.date.available2011-07-25T08:39:13Z
dc.date.issued2011-07-18
dc.identifierhttp://dx.doi.org/10.1186/1471-2202-12-S1-P152
dc.identifier.citationBMC Neuroscience. 2011 Jul 18;12(Suppl 1):P152
dc.identifier.urihttp://hdl.handle.net/10147/136799
dc.description.abstractnull
dc.description.abstractA novel method is presented for calculating the information channel capacity of spike trains. This method works by fitting a c-distribution to the distribution of distances between responses to the same stimulus: the c-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.
dc.description.sponsorshipScience Foundation Ireland
dc.titleA metric space approach to the information channel capacity of spike trains
dc.typeJournal Article
dc.language.rfc3066en
dc.rights.holderet al.; licensee BioMed Central Ltd.
dc.description.statusPeer Reviewed
dc.date.updated2011-07-22T20:31:11Z
refterms.dateFOA2018-08-22T13:11:08Z
html.description.abstractnull
html.description.abstractA novel method is presented for calculating the information channel capacity of spike trains. This method works by fitting a c-distribution to the distribution of distances between responses to the same stimulus: the c-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.


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