title: Dynamical criticality in the collective activity of a population of retinal neurons.
abstract: Recent experimental results based on multielectrode and imaging techniques have reinvigorated the idea that large neural networks operate near a critical point, between order and disorder. However, evidence for criticality has relied on the definition of arbitrary order parameters, or on models that do not address the dynamical nature of network activity. Here we introduce a novel approach to assess criticality that overcomes these limitations, while encompassing and generalizing previous criteria. We find a simple model to describe the global activity of large populations of ganglion cells in the rat retina, and show that their statistics are poised near a critical point. Taking into account the temporal dynamics of the activity greatly enhances the evidence for criticality, revealing it where previous methods would not. The approach is general and could be used in other biological networks.
authors.affiliation: Laboratoire de physique statistique, École normale supérieure, CNRS and UPMC, 24 rue Lhomond, 75005 Paris, France.
authors.forename: Thierry
authors.initials: T
authors.lastname: Mora
authors.affiliation: Institut de la Vision, INSERM and UMPC, 17 rue Moreau, 75012 Paris, France.
authors.forename: Stéphane
authors.initials: S
authors.lastname: Deny
authors.affiliation: Institut de la Vision, INSERM and UMPC, 17 rue Moreau, 75012 Paris, France.
authors.forename: Olivier
authors.initials: O
authors.lastname: Marre
mesh_terms: D000818:Animals; D008959:Models, Neurological; D051381:Rats; D012165:Retinal Ganglion Cells / Q000737:chemistry / Q000502:physiology*; D013816:Thermodynamics