title: Optimal integration of independent observations from Poisson sources.
abstract: The optimal integration of information from independent Poisson sources (such as neurons) was analyzed in the context of a two-interval, forced-choice detection task. When the mean count of the Poisson distribution is above 1, the benefit of integration is closely approximated by the predictions based on the square-root law of the Gaussian model. When the mean count falls far below 1, however, the benefit of integration clearly exceeds the predictions based on the square-root law.
authors.affiliation: Department of Speech, Language, and Hearing Sciences, University of Arizona, Tucson, Arizona 85721 hdai@email.arizona.edu.
authors.forename: Huanping
authors.initials: H
authors.lastname: Dai
authors.affiliation: Department of Otolaryngology/Head and Neck Surgery, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 ebuss@med.unc.edu.
authors.forename: Emily
authors.initials: E
authors.lastname: Buss
mesh_terms: D000465:Algorithms; D000818:Animals; D055641:Mathematical Concepts; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology; D016012:Poisson Distribution; D011571:Psychoacoustics; D017603:Signal Detection, Psychological