title: Binless strategies for estimation of information from neural data.
abstract: We present an approach to estimate information carried by experimentally observed neural spike trains elicited by known stimuli. This approach makes use of an embedding of the observed spike trains into a set of vector spaces, and entropy estimates based on the nearest-neighbor Euclidean distances within these vector spaces [L. F. Kozachenko and N. N. Leonenko, Probl. Peredachi Inf. 23, 9 (1987)]. Using numerical examples, we show that this approach can be dramatically more efficient than standard bin-based approaches such as the "direct" method [S. P. Strong, R. Koberle, R. R. de Ruyter van Steveninck, and W. Bialek, Phys. Rev. Lett. 80, 197 (1998)] for amounts of data typically available from laboratory experiments.
authors.affiliation: Department of Neurology and Neuroscience, Weill Medical College of Cornell University, 1300 York Avenue, New York, New York 10021, USA. jdvicto@med.cornell.edu
authors.forename: Jonathan D
authors.initials: JD
authors.lastname: Victor
mesh_terms: D000200:Action Potentials; D055592:Biophysical Phenomena; D001703:Biophysics; D019277:Entropy; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*