title: Transition to chaos in random networks with cell-type-specific connectivity.
abstract: In neural circuits, statistical connectivity rules strongly depend on cell-type identity. We study dynamics of neural networks with cell-type-specific connectivity by extending the dynamic mean-field method and find that these networks exhibit a phase transition between silent and chaotic activity. By analyzing the locus of this transition, we derive a new result in random matrix theory: the spectral radius of a random connectivity matrix with block-structured variances. We apply our results to show how a small group of hyperexcitable neurons within the network can significantly increase the network's computational capacity by bringing it into the chaotic regime.
authors.affiliation: Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, California 92037, USA and Center for Theoretical Biological Physics and Department of Physics, University of California, San Diego 92093, USA.
authors.forename: Johnatan
authors.initials: J
authors.lastname: Aljadeff
authors.affiliation: Department of Neuroscience, Columbia University, New York, New York 10032, USA and The Edmond and Lily Safra Center for Brain Sciences, Hebrew University, Jerusalem 9190401, Israel.
authors.forename: Merav
authors.initials: M
authors.lastname: Stern
authors.affiliation: Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, California 92037, USA and Center for Theoretical Biological Physics and Department of Physics, University of California, San Diego 92093, USA.
authors.forename: Tatyana
authors.initials: T
authors.lastname: Sharpee
mesh_terms: D023902:Chromosome Pairing / Q000502:physiology; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D009434:Neural Pathways / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics