Dynamical principles in neuroscience
- Institute for Nonlinear Science, University of California, San Diego, 9500 Gilman Drive 0402, La Jolla, California 92093-0402 (United States) and GNB, Departamento de Ingenieria Informatica, Universidad Autonoma de Madrid, 28049 Madrid, Spain and Institute for Nonlinear Science, University of California, San Diego, 9500 Gilman Drive 0402, La Jolla, California 92093-0402 (United States) and Institute for Nonlinear Science, University of California, San Diego, 9500 Gilman Drive 0402, La Jolla, California 92093-0402 (United States)
Dynamical modeling of neural systems and brain functions has a history of success over the last half century. This includes, for example, the explanation and prediction of some features of neural rhythmic behaviors. Many interesting dynamical models of learning and memory based on physiological experiments have been suggested over the last two decades. Dynamical models even of consciousness now exist. Usually these models and results are based on traditional approaches and paradigms of nonlinear dynamics including dynamical chaos. Neural systems are, however, an unusual subject for nonlinear dynamics for several reasons: (i) Even the simplest neural network, with only a few neurons and synaptic connections, has an enormous number of variables and control parameters. These make neural systems adaptive and flexible, and are critical to their biological function. (ii) In contrast to traditional physical systems described by well-known basic principles, first principles governing the dynamics of neural systems are unknown. (iii) Many different neural systems exhibit similar dynamics despite having different architectures and different levels of complexity. (iv) The network architecture and connection strengths are usually not known in detail and therefore the dynamical analysis must, in some sense, be probabilistic. (v) Since nervous systems are able to organize behavior based on sensory inputs, the dynamical modeling of these systems has to explain the transformation of temporal information into combinatorial or combinatorial-temporal codes, and vice versa, for memory and recognition. In this review these problems are discussed in the context of addressing the stimulating questions: What can neuroscience learn from nonlinear dynamics, and what can nonlinear dynamics learn from neuroscience?.
- OSTI ID:
- 21013701
- Journal Information:
- Reviews of Modern Physics, Vol. 78, Issue 4; Other Information: DOI: 10.1103/RevModPhys.78.1213; (c) 2006 The American Physical Society; Country of input: International Atomic Energy Agency (IAEA); ISSN 0034-6861
- Country of Publication:
- United States
- Language:
- English
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