title: Identification of functional information subgraphs in complex networks.
abstract: We present a general information theoretic approach for identifying functional subgraphs in complex networks. We show that the uncertainty in a variable can be written as a sum of information quantities, where each term is generated by successively conditioning mutual informations on new measured variables in a way analogous to a discrete differential calculus. The analogy to a Taylor series suggests efficient optimization algorithms for determining the state of a target variable in terms of functional groups of other nodes. We apply this methodology to electrophysiological recordings of cortical neuronal networks grown in vitro. Each cell's firing is generally explained by the activity of a few neurons. We identify these neuronal subgraphs in terms of their redundant or synergetic character and reconstruct neuronal circuits that account for the state of target cells.
authors.affiliation: T-7 and CNLS, Theoretical Division, MS B284 Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
authors.forename: Luís M A
authors.initials: LM
authors.lastname: Bettencourt
authors.forename: Vadas
authors.initials: V
authors.lastname: Gintautas
authors.forename: Michael I
authors.initials: MI
authors.lastname: Ham
mesh_terms: D000200:Action Potentials; D000818:Animals; D004594:Electrophysiology / Q000379:methods; D005625:Frontal Lobe / Q000502:physiology; D051379:Mice; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*