"Nonlinear Dynamics" is a descriptor in the National Library of Medicine's controlled vocabulary thesaurus,
MeSH (Medical Subject Headings). Descriptors are arranged in a hierarchical structure,
which enables searching at various levels of specificity.
The study of systems which respond disproportionately (nonlinearly) to initial conditions or perturbing stimuli. Nonlinear systems may exhibit "chaos" which is classically characterized as sensitive dependence on initial conditions. Chaotic systems, while distinguished from more ordered periodic systems, are not random. When their behavior over time is appropriately displayed (in "phase space"), constraints are evident which are described by "strange attractors". Phase space representations of chaotic systems, or strange attractors, usually reveal fractal (FRACTALS) self-similarity across time scales. Natural, including biological, systems often display nonlinear dynamics and chaos.
| Descriptor ID |
D017711
|
| MeSH Number(s) |
E05.599.850 H01.548.675
|
| Concept/Terms |
Nonlinear Dynamics- Nonlinear Dynamics
- Dynamics, Nonlinear
- Nonlinear Dynamic
- Non-linear Dynamics
- Dynamics, Non-linear
- Non linear Dynamics
- Non-linear Dynamic
Models, Nonlinear- Models, Nonlinear
- Model, Nonlinear
- Nonlinear Model
- Nonlinear Models
- Non-linear Models
- Model, Non-linear
- Models, Non-linear
- Non linear Models
- Non-linear Model
Chaos Theory- Chaos Theory
- Chaos Theories
- Theories, Chaos
- Theory, Chaos
|
Below are MeSH descriptors whose meaning is more general than "Nonlinear Dynamics".
Below are MeSH descriptors whose meaning is more specific than "Nonlinear Dynamics".
This graph shows the total number of publications written about "Nonlinear Dynamics" by people in this website by year, and whether "Nonlinear Dynamics" was a major or minor topic of these publications.
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Below are the most recent publications written about "Nonlinear Dynamics" by people in Profiles.
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Topolski S, Sturmberg J. Validation of a non-linear model of health. J Eval Clin Pract. 2014 Dec; 20(6):1026-35.
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Rissanen SM, Kankaanp?? M, Tarvainen MP, Novak V, Novak P, Hu K, Manor B, Airaksinen O, Karjalainen PA. Analysis of EMG and acceleration signals for quantifying the effects of deep brain stimulation in Parkinson's disease. IEEE Trans Biomed Eng. 2011 Sep; 58(9):2545-53.
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Vel?zquez-Marrero C, Wynne P, Bernardo A, Palacio S, Martin G, Treistman SN. The relationship between duration of initial alcohol exposure and persistence of molecular tolerance is markedly nonlinear. J Neurosci. 2011 Feb 16; 31(7):2436-46.
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Indic P, Narayanan J. Frequency flow dynamics of epileptic brain. Int J Neurosci. 2010 Mar; 120(3):222-5.
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Liu Z, Rios C, Zhang N, Yang L, Chen W, He B. Linear and nonlinear relationships between visual stimuli, EEG and BOLD fMRI signals. Neuroimage. 2010 Apr 15; 50(3):1054-66.
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Bloch-Salisbury E, Indic P, Bednarek F, Paydarfar D. Stabilizing immature breathing patterns of preterm infants using stochastic mechanosensory stimulation. J Appl Physiol (1985). 2009 Oct; 107(4):1017-27.
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Indic P, Schwartz WJ, Paydarfar D. Design principles for phase-splitting behaviour of coupled cellular oscillators: clues from hamsters with 'split' circadian rhythms. J R Soc Interface. 2008 Aug 06; 5(25):873-83.
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Zhang N, Zhu XH, Chen W. Investigating the source of BOLD nonlinearity in human visual cortex in response to paired visual stimuli. Neuroimage. 2008 Nov 01; 43(2):204-12.
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Rex DE, Ma JQ, Toga AW. The LONI Pipeline Processing Environment. Neuroimage. 2003 Jul; 19(3):1033-48.
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Indic P, Pratap R, Nampoori VP, Pradhan N. Significance of time scales in nonlinear dynamical analysis of electroencephalogram signals. Int J Neurosci. 1999 Aug; 99(1-4):181-94.