Literature DB >> 7936189

The brain as a dynamic physical system.

T M McKenna1, T A McMullen, M F Shlesinger.   

Abstract

The brain is a dynamic system that is non-linear at multiple levels of analysis. Characterization of its non-linear dynamics is fundamental to our understanding of brain function. Identifying families of attractors in phase space analysis, an approach which has proven valuable in describing non-linear mechanical and electrical systems, can prove valuable in describing a range of behaviors and associated neural activity including sensory and motor repertoires. Additionally, transitions between attractors may serve as useful descriptors for analysing state changes in neurons and neural ensembles. Recent observations of synchronous neural activity, and the emerging capability to record the spatiotemporal dynamics of neural activity by voltage-sensitive dyes and electrode arrays, provide opportunities for observing the population dynamics of neural ensembles within a dynamic systems context. New developments in the experimental physics of complex systems, such as the control of chaotic systems, selection of attractors, attractor switching and transient states, can be a source of powerful new analytical tools and insights into the dynamics of neural systems.

Mesh:

Year:  1994        PMID: 7936189     DOI: 10.1016/0306-4522(94)90489-8

Source DB:  PubMed          Journal:  Neuroscience        ISSN: 0306-4522            Impact factor:   3.590


  20 in total

Review 1.  Bring the Noise: Reconceptualizing Spontaneous Neural Activity.

Authors:  Lucina Q Uddin
Journal:  Trends Cogn Sci       Date:  2020-06-27       Impact factor: 20.229

2.  Dynamical heterogeneity of suprachiasmatic nucleus neurons based on regularity and determinism.

Authors:  Jaeseung Jeong; Yongho Kwak; Yang In Kim; Kyoung J Lee
Journal:  J Comput Neurosci       Date:  2005-08       Impact factor: 1.621

3.  A Graph-Based Nonlinear Dynamic Characterization of Motor Imagery Toward an Enhanced Hybrid BCI.

Authors:  Sarah M I Hosni; Seyyed B Borgheai; John McLinden; Shaotong Zhu; Xiaofei Huang; Sarah Ostadabbas; Yalda Shahriari
Journal:  Neuroinformatics       Date:  2022-07-30

Review 4.  Neuromodulation: selected approaches and challenges.

Authors:  Vladimir Parpura; Gabriel A Silva; Peter A Tass; Kevin E Bennet; M Meyyappan; Jessica Koehne; Kendall H Lee; Russell J Andrews
Journal:  J Neurochem       Date:  2012-12-26       Impact factor: 5.372

5.  Movement Enhances the Nonlinearity of Hippocampal Theta.

Authors:  Alex Sheremet; Sara N Burke; Andrew P Maurer
Journal:  J Neurosci       Date:  2016-04-13       Impact factor: 6.167

6.  Letting the brain speak for itself.

Authors:  Gerhard Werner
Journal:  Front Physiol       Date:  2011-09-22       Impact factor: 4.566

7.  Higher-order spectrum in understanding nonlinearity in EEG rhythms.

Authors:  Cauchy Pradhan; Susant K Jena; Sreenivasan R Nadar; N Pradhan
Journal:  Comput Math Methods Med       Date:  2012-02-08       Impact factor: 2.238

8.  Fundamental dynamical modes underlying human brain synchronization.

Authors:  Catalina Alvarado-Rojas; Michel Le Van Quyen
Journal:  Comput Math Methods Med       Date:  2012-06-28       Impact factor: 2.238

9.  Nonlinear dynamics based digital logic and circuits.

Authors:  Behnam Kia; John F Lindner; William L Ditto
Journal:  Front Comput Neurosci       Date:  2015-05-15       Impact factor: 2.380

Review 10.  Non-linear dynamics in parkinsonism.

Authors:  Olivier Darbin; Elizabeth Adams; Anthony Martino; Leslie Naritoku; Daniel Dees; Dean Naritoku
Journal:  Front Neurol       Date:  2013-12-25       Impact factor: 4.003

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.