Binless strategies for estimation of information from neural data. Academic Article uri icon

Overview

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.

publication date

  • November 11, 2002

Research

keywords

  • Models, Neurological
  • Neurons

Identity

Scopus Document Identifier

  • 85036170213

Digital Object Identifier (DOI)

  • 10.1103/PhysRevE.66.051903

PubMed ID

  • 12513519

Additional Document Info

volume

  • 66

issue

  • 5 Pt 1