Dynamical neuroscience
branch of mathematical biology
The dynamical systems approach to neuroscience is a branch of mathematical biology that utilizes nonlinear dynamics to understand and model the nervous system and its functions. In a dynamical system, all possible states are expressed by a phase space. Such systems can experience bifurcation (a qualitative change in behavior) as a function of its bifurcation parameters and often exhibit chaos. Dynamical neuroscience describes the non-linear dynamics at many levels of the brain from single neural cells to cognitive processes, sleep states and the behavior of neurons in large-scale neuronal simulation.
Neurons have been modeled as nonlinear systems for decades, but dynamical systems are not constrained to neurons. Dynamical systems can emerge in other ways in the nervous system. Chemical species models, like the Gray–Scott model, can exhibit rich, chaotic dynamics. Intraneural communication is affected by dynamic interactions between extracellular fluid pathways. Information theory draws on thermodynamics in the development of infodynamics that can involve nonlinear systems, especially with regards to the brain.
History
One of the earliest models of the neuron was based on mathematical and physical modelling: the integrate-and-fire model, which was developed in 1907.
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This entry incorporates text from “Dynamical neuroscience” on English Wikipedia. Contributors are listed in the page history. Text is available under the Creative Commons Attribution-ShareAlike 4.0 License. Selected authority identifiers and statements are retrieved from Wikidata under CC0; their references and qualifiers remain part of the verification path.