Global ETD Search

1	Finite-time Lyapunov exponents and metabolic control coefficients for threshold detection of stimulus–response curves Luu, Hoang Duc, Chávez , Joseph Páez, Son, Doan Thai, Siegmund, Stefan 19 December 2016 (has links) (PDF) In biochemical networks transient dynamics plays a fundamental role, since the activation of signalling pathways is determined by thresholds encountered during the transition from an initial state (e.g. an initial concentration of a certain protein) to a steady-state. These thresholds can be defined in terms of the inflection points of the stimulus-response curves associated to the activation processes in the biochemical network. In the present work, we present a rigorous discussion as to the suitability of finite-time Lyapunov exponents and metabolic control coefficients for the detection of inflection points of stimulus-response curves with sigmoidal shape. 37N25 Finite-time Lyapunov-Spektrum nichtautonomes dynamisches System sigmoidale Ansprechkurve transiente Dynamik TU Dresden Publikationsfonds 37N25 Finite-time Lyapunov spectrum nonautonomous dynamical system sigmoidal response curve transient dynamics TU Dresden publication fund ddc:570 rvk:SA 1075
2	Finite-time Lyapunov exponents and metabolic control coefficients for threshold detection of stimulus–response curves Luu, Hoang Duc, Chávez, Joseph Páez, Son, Doan Thai, Siegmund, Stefan 19 December 2016 (has links) In biochemical networks transient dynamics plays a fundamental role, since the activation of signalling pathways is determined by thresholds encountered during the transition from an initial state (e.g. an initial concentration of a certain protein) to a steady-state. These thresholds can be defined in terms of the inflection points of the stimulus-response curves associated to the activation processes in the biochemical network. In the present work, we present a rigorous discussion as to the suitability of finite-time Lyapunov exponents and metabolic control coefficients for the detection of inflection points of stimulus-response curves with sigmoidal shape. info:eu-repo/classification/ddc/570 ddc:570
3	Noise, Delays, and Resonance in a Neural Network Quan, Austin 01 May 2011 (has links) A stochastic-delay differential equation (SDDE) model of a small neural network with recurrent inhibition is presented and analyzed. The model exhibits unexpected transient behavior: oscillations that occur at the boundary of the basins of attraction when the system is bistable. These are known as delay-induced transitory oscillations (DITOs). This behavior is analyzed in the context of stochastic resonance, an unintuitive, though widely researched phenomenon in physical bistable systems where noise can play in constructive role in strengthening an input signal. A method for modeling the dynamics using a probabilistic three-state model is proposed, and supported with numerical evidence. The potential implications of this dynamical phenomenon to nocturnal frontal lobe epilepsy (NFLE) are also discussed. 37N25 Dynamical systems in biology Artificial Intelligence and Robotics Dynamical Systems Dynamic Systems Medical Neurobiology

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