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INVITED REVIEW
1Department of Statistics and Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania; and 2Neuroscience Research Laboratory, Department of Anesthesia and Critical Care, Massachusetts General Hospital, and the Division of Health Sciences and Technology, Harvard Medical School, Massachusetts Institute of Technology, Boston, Massachusetts
Submitted 25 June 2004; accepted in final form 19 February 2005
ABSTRACT
Analysis of data from neurophysiological investigations can be challenging. Particularly when experiments involve dynamics of neuronal response, scientific inference can become subtle and some statistical methods may make much more efficient use of the data than others. This article reviews well-established statistical principles, which provide useful guidance, and argues that good statistical practice can substantially enhance results. Recent work on estimation of firing rate, population coding, and time-varying correlation provides improvements in experimental sensitivity equivalent to large increases in the number of neurons examined. Modern nonparametric methods are applicable to data from repeated trials. Many within-trial analyses based on a Poisson assumption can be extended to non-Poisson data. New methods have made it possible to track changes in receptive fields, and to study trial-to-trial variation, with modest amounts of data.
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