title: Origin of information-limiting noise correlations.
abstract: The ability to discriminate between similar sensory stimuli relies on the amount of information encoded in sensory neuronal populations. Such information can be substantially reduced by correlated trial-to-trial variability. Noise correlations have been measured across a wide range of areas in the brain, but their origin is still far from clear. Here we show analytically and with simulations that optimal computation on inputs with limited information creates patterns of noise correlations that account for a broad range of experimental observations while at same time causing information to saturate in large neural populations. With the example of a network of V1 neurons extracting orientation from a noisy image, we illustrate to our knowledge the first generative model of noise correlations that is consistent both with neurophysiology and with behavioral thresholds, without invoking suboptimal encoding or decoding or internal sources of variability such as stochastic network dynamics or cortical state fluctuations. We further show that when information is limited at the input, both suboptimal connectivity and internal fluctuations could similarly reduce the asymptotic information, but they have qualitatively different effects on correlations leading to specific experimental predictions. Our study indicates that noise at the sensory periphery could have a major effect on cortical representations in widely studied discrimination tasks. It also provides an analytical framework to understand the functional relevance of different sources of experimentally measured correlations.
authors.affiliation: Department of Basic Neuroscience, University of Geneva, 1211 Geneva, Switzerland; Center of Learning and Memory and Department of Neuroscience, The University of Texas at Austin, Austin, TX 78712; IKanitscheider@mail.clm.utexas.edu.
authors.forename: Ingmar
authors.initials: I
authors.lastname: Kanitscheider
authors.affiliation: Department of Basic Neuroscience, University of Geneva, 1211 Geneva, Switzerland;
authors.forename: Ruben
authors.initials: R
authors.lastname: Coen-Cagli
authors.affiliation: Department of Basic Neuroscience, University of Geneva, 1211 Geneva, Switzerland; Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14627; Gatsby Computational Neuroscience Unit, University College London, London W1T 4JG, United Kingdom.
authors.forename: Alexandre
authors.initials: A
authors.lastname: Pouget
mesh_terms: D000200:Action Potentials; D000818:Animals; D006801:Humans; D009622:Noise; D011984:Sensory Receptor Cells / Q000502:physiology*
keywords: efficient coding; information theory; neural computation; neuronal variability; noise correlations