title: Optimal neuronal tuning for finite stimulus spaces.
abstract: The efficiency of neuronal encoding in sensory and motor systems has been proposed as a first principle governing response properties within the central nervous system. We present a continuation of a theoretical study presented by Zhang and Sejnowski, where the influence of neuronal tuning properties on encoding accuracy is analyzed using information theory. When a finite stimulus space is considered, we show that the encoding accuracy improves with narrow tuning for one- and two-dimensional stimuli. For three dimensions and higher, there is an optimal tuning width.
authors.affiliation: Computational Biology, Sandia National Laboratories, Albuquerque, NM 87123, USA. wmbrown@sandia.gov
authors.forename: W Michael
authors.initials: WM
authors.lastname: Brown
authors.forename: Alex
authors.initials: A
authors.lastname: Bäcker
mesh_terms: D000200:Action Potentials / Q000502:physiology; D003198:Computer Simulation; D006801:Humans; D007257:Information Theory; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D009949:Orientation