Browsing by Subject "nonlinear dynamics"

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    Parity-time symmetry breaking in optically coupled semiconductor lasers
    (SPIE, 2016-09) Suelzer, Joseph S.; Joglekar, Yogesh N.; Vemuri, Gautam; Department of Physics, School of Science
    We experimentally demonstrate the realization of a parity-time (PT) symmetry breaking in optically coupled semiconductor lasers (SCLs). The two SCLs are identical except for a detuning between their optical emission frequencies. This detuning is analogous to the gain-loss parameter found in optical PT systems. To model the coupled SCLs, we employ the standard rate equations describing the electric field and carrier inversion of each SCL, and show that, under certain conditions, the rate equations reduce to the canonical, two-site PT- symmetric model. This model captures the global behavior of the laser intensity as the system parameters are varied. Overall, we find that this bulk system (coupled SCLs) provides an excellent test-bed to probe the characteristics of PT-breaking transitions, including the effects of time delay.
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    Synchronization and long-time memory in neural networks with inhibitory hubs and synaptic plasticity
    (APS, 2017-01) Bertolotti, Elena; Burioni, Raffaella; di Volo, Matteo; Vezzani, Alessandro; Department of Mathematical Sciences, School of Science
    We investigate the dynamical role of inhibitory and highly connected nodes (hub) in synchronization and input processing of leaky-integrate-and-fire neural networks with short term synaptic plasticity. We take advantage of a heterogeneous mean-field approximation to encode the role of network structure and we tune the fraction of inhibitory neurons f I and their connectivity level to investigate the cooperation between hub features and inhibition. We show that, depending on f I , highly connected inhibitory nodes strongly drive the synchronization properties of the overall network through dynamical transitions from synchronous to asynchronous regimes. Furthermore, a metastable regime with long memory of external inputs emerges for a specific fraction of hub inhibitory neurons, underlining the role of inhibition and connectivity also for input processing in neural networks.