Calculating mutual information for spike trains and other data with distances but no coordinates

dc.contributor.author	Houghton, Conor	en_US
dc.date.accessioned	2016-07-30T01:24:41Z
dc.date.available	2016-07-30T01:24:41Z
dc.date.issued	2015	en_US
dc.identifier.other	HPU4160481	en_US
dc.identifier.uri	https://lib.hpu.edu.vn/handle/123456789/22376
dc.description.abstract	Many important data types, such as the spike trains recorded from neurons in typical electrophysiological experiments, have a natural notion of distance or similarity between data points, even though there is no obvious coordinate system. Here, a simple Kozachenko–Leonenko estimator is derived for calculating the mutual information between datasets of this type.	en_US
dc.format.extent	6 p.	en_US
dc.format.mimetype	application/pdf
dc.language.iso	en	en_US
dc.subject	Mathematics	en_US
dc.subject	Appliedmathematics	en_US
dc.subject	Neuroscience	en_US
dc.subject	Computational biology	en_US
dc.subject	Spike trains	en_US
dc.subject	Information theory	en_US
dc.subject	Mutual information	en_US
dc.title	Calculating mutual information for spike trains and other data with distances but no coordinates	en_US
dc.type	Article	en_US
dc.size	325KB	en_US
dc.department	Education	en_US