title: Information-limiting correlations.
abstract: Computational strategies used by the brain strongly depend on the amount of information that can be stored in population activity, which in turn strongly depends on the pattern of noise correlations. In vivo, noise correlations tend to be positive and proportional to the similarity in tuning properties. Such correlations are thought to limit information, which has led to the suggestion that decorrelation increases information. In contrast, we found, analytically and numerically, that decorrelation does not imply an increase in information. Instead, the only information-limiting correlations are what we refer to as differential correlations: correlations proportional to the product of the derivatives of the tuning curves. Unfortunately, differential correlations are likely to be very small and buried under correlations that do not limit information, making them particularly difficult to detect. We found, however, that the effect of differential correlations on information can be detected with relatively simple decoders.
authors.affiliation: 1] Research Unit, Parc Sanitari Sant Joan de Déu and Universitat de Barcelona, Esplugues de Llobregat, Barcelona, Spain. [2] Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Esplugues de Llobregat, Barcelona, Spain.
authors.forename: Rubén
authors.initials: R
authors.lastname: Moreno-Bote
authors.affiliation: Department of Brain and Cognitive Sciences, University of Rochester, Rochester, New York, USA.
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Beck
authors.affiliation: Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland.
authors.forename: Ingmar
authors.initials: I
authors.lastname: Kanitscheider
authors.affiliation: 1] Department of Brain and Cognitive Sciences, University of Rochester, Rochester, New York, USA. [2] Department of Neuroscience, Baylor College of Medicine, Houston, Texas, USA. [3] Department of Electrical and Computer Engineering, Rice University, Houston, Texas, USA.
authors.forename: Xaq
authors.initials: X
authors.lastname: Pitkow
authors.affiliation: Gatsby Computational Neuroscience Unit, University College London, London, UK.
authors.forename: Peter
authors.initials: P
authors.lastname: Latham
authors.affiliation: 1] Department of Brain and Cognitive Sciences, University of Rochester, Rochester, New York, USA. [2] Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland. [3] Gatsby Computational Neuroscience Unit, University College London, London, UK.
authors.forename: Alexandre
authors.initials: A
authors.lastname: Pouget
mesh_terms: D001921:Brain / Q000166:cytology; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D013223:Statistics as Topic