title: Dynamics of random neural networks with bistable units.
abstract: We construct and analyze a rate-based neural network model in which self-interacting units represent clusters of neurons with strong local connectivity and random interunit connections reflect long-range interactions. When sufficiently strong, the self-interactions make the individual units bistable. Simulation results, mean-field calculations, and stability analysis reveal the different dynamic regimes of this network and identify the locations in parameter space of its phase transitions. We identify an interesting dynamical regime exhibiting transient but long-lived chaotic activity that combines features of chaotic and multiple fixed-point attractors.
authors.affiliation: Edmond and Lily Safra Center for Brain Sciences, Hebrew University, Jerusalem, Israel and Department of Neuroscience, Columbia University College of Physicians and Surgeons, New York, New York 10032-2695, USA.
authors.forename: M
authors.initials: M
authors.lastname: Stern
authors.affiliation: Edmond and Lily Safra Center for Brain Sciences, Hebrew University, Jerusalem, Israel and Racah Institute of Physics, Hebrew University, Jerusalem, Israel and Center for Brain Science, Harvard University, Cambridge, Massachusetts 02138, USA.
authors.forename: H
authors.initials: H
authors.lastname: Sompolinsky
authors.affiliation: Department of Neuroscience, Columbia University College of Physicians and Surgeons, New York, New York 10032-2695, USA and Department of Physiology and Cellular Biophysics, Columbia University College of Physicians and Surgeons, New York, New York 10032-2695, USA.
authors.forename: L F
authors.initials: LF
authors.lastname: Abbott
mesh_terms: D008959:Models, Neurological; D009415:Nerve Net / Q000166:cytology; D009474:Neurons / Q000166:cytology; D013269:Stochastic Processes; D013997:Time Factors