title: Neuronal oscillations and stochastic limit cycles.
abstract: We investigate a model for synchronous neural activity in networks of coupled neurons. The individual systems are governed by nonlinear dynamics and can continuously vary between excitable and oscillatory behavior. Analytical calculations and computer simulations show that coupled excitable systems can undergo two different phase transitions from synchronous to asynchronous firing behavior. One of the transitions is akin to the synchronization transitions in coupled oscillator systems, while the second transition can only be found in coupled excitable systems. Using the concept of Stochastic Limit Cycles, we present an analytical derivation of the two transitions and discuss implications for synchronization transitions in biological neural networks.
authors.affiliation: Department of Physics and Beckman Institute, University of Illinois at Urbana-Champaign 61801, USA. kurrer@ton.scphys.kyoto-u.ac.jp
authors.forename: C
authors.initials: C
authors.lastname: Kurrer
authors.forename: K
authors.initials: K
authors.lastname: Schulten
mesh_terms: D001683:Biological Clocks / Q000502:physiology*; D009415:Nerve Net; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D013269:Stochastic Processes