title: Toward functional classification of neuronal types.
abstract: How many types of neurons are there in the brain? This basic neuroscience question remains unsettled despite many decades of research. Classification schemes have been proposed based on anatomical, electrophysiological, or molecular properties. However, different schemes do not always agree with each other. This raises the question of whether one can classify neurons based on their function directly. For example, among sensory neurons, can a classification scheme be devised that is based on their role in encoding sensory stimuli? Here, theoretical arguments are outlined for how this can be achieved using information theory by looking at optimal numbers of cell types and paying attention to two key properties: correlations between inputs and noise in neural responses. This theoretical framework could help to map the hierarchical tree relating different neuronal classes within and across species.
authors.affiliation: Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA. Electronic address: sharpee@salk.edu.
authors.forename: Tatyana O
authors.initials: TO
authors.lastname: Sharpee
mesh_terms: D000818:Animals; D001921:Brain / Q000166:cytology* / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D009474:Neurons / Q000145:classification* / Q000166:cytology / Q000502:physiology