Statistical issues in the analysis of neuronal data.
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.
Department of Statistics and Center for the Neural Basis of Cognition, 5000 Forbes Ave., Baker Hall 132 Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA.
Robert E
RE
Kass
Valérie
V
Ventura
Emery N
EN
Brown
D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D009474:Neurons / Q000502:physiology*; D012680:Sensitivity and Specificity; D013997:Time Factors