title: Structures of Neural Correlation and How They Favor Coding.
abstract: The neural representation of information suffers from "noise"-the trial-to-trial variability in the response of neurons. The impact of correlated noise upon population coding has been debated, but a direct connection between theory and experiment remains tenuous. Here, we substantiate this connection and propose a refined theoretical picture. Using simultaneous recordings from a population of direction-selective retinal ganglion cells, we demonstrate that coding benefits from noise correlations. The effect is appreciable already in small populations, yet it is a collective phenomenon. Furthermore, the stimulus-dependent structure of correlation is key. We develop simple functional models that capture the stimulus-dependent statistics. We then use them to quantify the performance of population coding, which depends upon interplays of feature sensitivities and noise correlations in the population. Because favorable structures of correlation emerge robustly in circuits with noisy, nonlinear elements, they will arise and benefit coding beyond the confines of retina.
authors.affiliation: Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland; Department of Physics, Ecole Normale Supérieure, 75005 Paris, France; Laboratoire de Physique Statistique, Centre National de la Recherche Scientifique, Université Pierre et Marie Curie, Université Denis Diderot, 75005 Paris, France.
authors.forename: Felix
authors.initials: F
authors.lastname: Franke
authors.affiliation: Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland.
authors.forename: Michele
authors.initials: M
authors.lastname: Fiscella
authors.affiliation: Department of Physics, Ecole Normale Supérieure, 75005 Paris, France; Laboratoire de Physique Statistique, Centre National de la Recherche Scientifique, Université Pierre et Marie Curie, Université Denis Diderot, 75005 Paris, France.
authors.forename: Maksim
authors.initials: M
authors.lastname: Sevelev
authors.affiliation: Friedrich Miescher Institute, 4058 Basel, Switzerland.
authors.forename: Botond
authors.initials: B
authors.lastname: Roska
authors.affiliation: Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland.
authors.forename: Andreas
authors.initials: A
authors.lastname: Hierlemann
authors.affiliation: Department of Physics, Ecole Normale Supérieure, 75005 Paris, France; Laboratoire de Physique Statistique, Centre National de la Recherche Scientifique, Université Pierre et Marie Curie, Université Denis Diderot, 75005 Paris, France; Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA. Electronic address: rava@ens.fr.
authors.forename: Rava Azeredo
authors.initials: RA
authors.lastname: da Silveira
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D005260:Female; D008959:Models, Neurological; D009415:Nerve Net / Q000166:cytology / Q000502:physiology*; D011817:Rabbits; D012165:Retinal Ganglion Cells / Q000502:physiology*