title: Correlations and Neuronal Population Information.
abstract: Brain function involves the activity of neuronal populations. Much recent effort has been devoted to measuring the activity of neuronal populations in different parts of the brain under various experimental conditions. Population activity patterns contain rich structure, yet many studies have focused on measuring pairwise relationships between members of a larger population-termed noise correlations. Here we review recent progress in understanding how these correlations affect population information, how information should be quantified, and what mechanisms may give rise to correlations. As population coding theory has improved, it has made clear that some forms of correlation are more important for information than others. We argue that this is a critical lesson for those interested in neuronal population responses more generally: Descriptions of population responses should be motivated by and linked to well-specified function. Within this context, we offer suggestions of where current theoretical frameworks fall short.
authors.affiliation: Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York 10461; email: adam.kohn@einstein.yu.edu.|Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, New York 10461.
authors.forename: Adam
authors.initials: A
authors.lastname: Kohn
authors.affiliation: Department of Basic Neuroscience, University of Geneva, CH-1211 Geneva, Switzerland; email: Ruben.CoenCagli@unige.ch , alexandre.pouget@unige.ch.
authors.forename: Ruben
authors.initials: R
authors.lastname: Coen-Cagli
authors.affiliation: Department of Basic Neuroscience, University of Geneva, CH-1211 Geneva, Switzerland; email: Ruben.CoenCagli@unige.ch , alexandre.pouget@unige.ch.|Center of Learning and Memory, The University of Texas at Austin, Austin, Texas 78712; email: IKanitscheider@mail.clm.utexas.edu.|Department of Neuroscience, The University of Texas at Austin, Austin, Texas 78712.
authors.forename: Ingmar
authors.initials: I
authors.lastname: Kanitscheider
authors.affiliation: Department of Basic Neuroscience, University of Geneva, CH-1211 Geneva, Switzerland; email: Ruben.CoenCagli@unige.ch , alexandre.pouget@unige.ch.|Department of Brain and Cognitive Sciences, University of Rochester, Rochester, New York 14627.|Gatsby Computational Neuroscience Unit, University College London, W1T 4JG London, United Kingdom.
authors.forename: Alexandre
authors.initials: A
authors.lastname: Pouget
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D013223:Statistics as Topic / Q000379:methods
keywords: Fisher information; decoding; neural coding; neural variability; perception; theoretical neuroscience