Works matching AU Young, Lai-Sang
Results: 34
Understanding Chaotic Dynamical Systems.
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- Communications on Pure & Applied Mathematics, 2013, v. 66, n. 9, p. 1439, doi. 10.1002/cpa.21468
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Lyapunov Exponents and Correlation Decay for Random Perturbations of Some Prototypical 2D Maps.
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- Communications in Mathematical Physics, 2018, v. 359, n. 1, p. 347, doi. 10.1007/s00220-017-2999-2
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Absolute Continuity of Stable Foliations for Mappings of Banach Spaces.
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- Communications in Mathematical Physics, 2017, v. 354, n. 2, p. 591, doi. 10.1007/s00220-017-2912-z
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Dispersing Billiards with Moving Scatterers.
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- Communications in Mathematical Physics, 2013, v. 322, n. 3, p. 909, doi. 10.1007/s00220-013-1746-6
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Dynamical profile of a class of rank-one attractors.
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- Ergodic Theory & Dynamical Systems, 2013, v. 33, n. 4, p. 1221, doi. 10.1017/S014338571200020X
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Entropy, Lyapunov exponents and escape rates in open systems.
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- Ergodic Theory & Dynamical Systems, 2012, v. 32, n. 4, p. 1270, doi. 10.1017/S0143385711000344
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Nonexistence of SBR measures for some diffeomorphisms that are ‘Almost Anosov’.
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- Ergodic Theory & Dynamical Systems, 1995, v. 15, n. 1, p. 67, doi. 10.1017/S0143385700008245
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Absolutely continuous invariant measures and random perturbations for certain one-dimensional maps.
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- Ergodic Theory & Dynamical Systems, 1992, v. 12, n. 1, p. 13, doi. 10.1017/S0143385700006556
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Stochastic stability of hyperbolic attractors.
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- Ergodic Theory & Dynamical Systems, 1986, v. 6, n. 2, p. 311, doi. 10.1017/S0143385700003473
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Dimension, entropy and Lyapunov exponents.
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- Ergodic Theory & Dynamical Systems, 1982, v. 2, n. 1, p. 109, doi. 10.1017/S0143385700009615
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Capacity of attractors.
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- Ergodic Theory & Dynamical Systems, 1981, v. 1, n. 3, p. 381, doi. 10.1017/S0143385700001309
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Consequences of delays and imperfect implementation of isolation in epidemic control.
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- Scientific Reports, 2019, v. 9, n. 1, p. 1, doi. 10.1038/s41598-019-39714-0
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Nonuniformly hyperbolic systems arising from coupling of chaotic and gradient-like systems.
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- Discrete & Continuous Dynamical Systems: Series A, 2020, v. 40, n. 10, p. 6015, doi. 10.3934/dcds.2020257
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A data-informed mean-field approach to mapping of cortical parameter landscapes.
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- PLoS Computational Biology, 2021, v. 17, n. 12, p. 1, doi. 10.1371/journal.pcbi.1009718
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Towards a Mathematical Model of the Brain.
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- Journal of Statistical Physics, 2020, v. 180, n. 1-6, p. 612, doi. 10.1007/s10955-019-02483-1
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Generalizations of SRB Measures to Nonautonomous, Random, and Infinite Dimensional Systems.
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- Journal of Statistical Physics, 2017, v. 166, n. 3/4, p. 494, doi. 10.1007/s10955-016-1639-0
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Local Thermal Equilibrium for Certain Stochastic Models of Heat Transport.
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- Journal of Statistical Physics, 2016, v. 163, n. 1, p. 61, doi. 10.1007/s10955-016-1466-3
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Existence of Nonequilibrium Steady State for a Simple Model of Heat Conduction.
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- Journal of Statistical Physics, 2013, v. 152, n. 6, p. 1170, doi. 10.1007/s10955-013-0801-1
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Nonequilibrium Steady States of Some Simple 1-D Mechanical Chains.
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- Journal of Statistical Physics, 2012, v. 146, n. 5, p. 1089, doi. 10.1007/s10955-012-0437-6
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A case study in the functional consequences of scaling the sizes of realistic cortical models.
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- PLoS Computational Biology, 2019, v. 15, n. 7, p. 1, doi. 10.1371/journal.pcbi.1007198
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DNN-assisted statistical analysis of a model of local cortical circuits.
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- Scientific Reports, 2020, v. 10, n. 1, p. N.PAG, doi. 10.1038/s41598-020-76770-3
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Reliability of Coupled Oscillators.
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- Journal of Nonlinear Science, 2009, v. 19, n. 5, p. 497, doi. 10.1007/s00332-009-9042-5
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Three pre-vaccine responses to Covid-like epidemics.
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- PLoS ONE, 2021, v. 16, n. 5, p. 1, doi. 10.1371/journal.pone.0251349
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The Use of Reduced Models to Generate Irregular, Broad-Band Signals That Resemble Brain Rhythms.
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- Frontiers in Computational Neuroscience, 2022, v. 16, p. 1, doi. 10.3389/fncom.2022.889235
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Malleability of gamma rhythms enhances population-level correlations.
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- Journal of Computational Neuroscience, 2021, v. 49, n. 2, p. 189, doi. 10.1007/s10827-021-00779-4
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Emergent spike patterns in neuronal populations.
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- Journal of Computational Neuroscience, 2015, v. 38, n. 1, p. 203, doi. 10.1007/s10827-014-0534-4
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Emergent dynamics in a model of visual cortex.
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- Journal of Computational Neuroscience, 2013, v. 35, n. 2, p. 155, doi. 10.1007/s10827-013-0445-9
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Dynamics of spiking neurons: between homogeneity and synchrony.
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- Journal of Computational Neuroscience, 2013, v. 34, n. 3, p. 433, doi. 10.1007/s10827-012-0429-1
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An SIQ delay differential equations model for disease control via isolation.
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- Journal of Mathematical Biology, 2019, v. 79, n. 1, p. 249, doi. 10.1007/s00285-019-01356-1
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Entropy, volume growth and SRB measures for Banach space mappings.
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- Inventiones Mathematicae, 2017, v. 207, n. 2, p. 833, doi. 10.1007/s00222-016-0678-0
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Unraveling the mechanisms of surround suppression in early visual processing.
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- PLoS Computational Biology, 2021, v. 17, n. 4, p. 1, doi. 10.1371/journal.pcbi.1008916
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Correlations in Nonequilibrium Steady States of Random Halves Models.
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- Journal of Statistical Physics, 2007, v. 128, n. 3, p. 607, doi. 10.1007/s10955-007-9318-9
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How well do reduced models capture the dynamics in models of interacting neurons?
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- Journal of Mathematical Biology, 2019, v. 78, n. 1/2, p. 83, doi. 10.1007/s00285-018-1268-0
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Limitations of perturbative techniques in the analysis of rhythms and oscillations.
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- Journal of Mathematical Biology, 2013, v. 66, n. 1/2, p. 139, doi. 10.1007/s00285-012-0506-0
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