Literature DB >> 11558325

Is there chaos in the brain? I. Concepts of nonlinear dynamics and methods of investigation.

P Faure1, H Korn.   

Abstract

In the light of results obtained during the last two decades in a number of laboratories, it appears that some of the tools of nonlinear dynamics, first developed and improved for the physical sciences and engineering, are well-suited for studies of biological phenomena. In particular it has become clear that the different regimes of activities undergone by nerve cells, neural assemblies and behavioural patterns, the linkage between them, and their modifications over time, cannot be fully understood in the context of even integrative physiology, without using these new techniques. This report, which is the first of two related papers, is aimed at introducing the non expert to the fundamental aspects of nonlinear dynamics, the most spectacular aspect of which is chaos theory. After a general history and definition of chaos the principles of analysis of time series in phase space and the general properties of chaotic trajectories will be described as will be the classical measures which allow a process to be classified as chaotic in ideal systems and models. We will then proceed to show how these methods need to be adapted for handling experimental time series; the dangers and pitfalls faced when dealing with non stationary and often noisy data will be stressed, and specific criteria for suspecting determinism in neuronal cells and/or assemblies will be described. We will finally address two fundamental questions, namely i) whether and how can one distinguish, deterministic patterns from stochastic ones, and, ii) what is the advantage of chaos over randomness: we will explain why and how the former can be controlled whereas, notoriously, the latter cannot be tamed. In the second paper of the series, results obtained at the level of single cells and their membrane conductances in real neuronal networks and in the study of higher brain functions, will be critically reviewed. It will be shown that the tools of nonlinear dynamics can be irreplaceable for revealing hidden mechanisms subserving, for example, neuronal synchronization and periodic oscillations. The benefits for the brain of adopting chaotic regimes with their wide range of potential behaviours and their aptitude to quickly react to changing conditions will also be considered.

Mesh:

Year:  2001        PMID: 11558325     DOI: 10.1016/s0764-4469(01)01377-4

Source DB:  PubMed          Journal:  C R Acad Sci III        ISSN: 0764-4469


  24 in total

1.  Patients with Chronic Obstructive Pulmonary Disease Walk with Altered Step Time and Step Width Variability as Compared with Healthy Control Subjects.

Authors:  Jennifer M Yentes; Stephen I Rennard; Kendra K Schmid; Daniel Blanke; Nicholas Stergiou
Journal:  Ann Am Thorac Soc       Date:  2017-06

Review 2.  Human movement variability, nonlinear dynamics, and pathology: is there a connection?

Authors:  Nicholas Stergiou; Leslie M Decker
Journal:  Hum Mov Sci       Date:  2011-07-29       Impact factor: 2.161

3.  Differential regulation of observational fear and neural oscillations by serotonin and dopamine in the mouse anterior cingulate cortex.

Authors:  Byung Sun Kim; Junghee Lee; Minji Bang; Bo Am Seo; Arshi Khalid; Min Whan Jung; Daejong Jeon
Journal:  Psychopharmacology (Berl)       Date:  2014-04-22       Impact factor: 4.530

4.  Noise Shaping in Neural Populations with Global Delayed Feedback.

Authors:  O Ávila Åkerberg; M J Chacron
Journal:  Math Model Nat Phenom       Date:  2010-01-01       Impact factor: 4.157

5.  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

6.  Research on the relation of EEG signal chaos characteristics with high-level intelligence activity of human brain.

Authors:  Xingyuan Wang; Juan Meng; Guilin Tan; Lixian Zou
Journal:  Nonlinear Biomed Phys       Date:  2010-04-27

7.  A principal component network analysis of prefrontal-limbic functional magnetic resonance imaging time series in schizophrenia patients and healthy controls.

Authors:  Anca R Rădulescu; Lilianne R Mujica-Parodi
Journal:  Psychiatry Res       Date:  2009-11-02       Impact factor: 3.222

8.  Chaotic Boltzmann machines.

Authors:  Hideyuki Suzuki; Jun-ichi Imura; Yoshihiko Horio; Kazuyuki Aihara
Journal:  Sci Rep       Date:  2013       Impact factor: 4.379

9.  Spin-torque building blocks.

Authors:  N Locatelli; V Cros; J Grollier
Journal:  Nat Mater       Date:  2014-01       Impact factor: 43.841

10.  Stochasticity, Nonlinear Value Functions, and Update Rules in Learning Aesthetic Biases.

Authors:  Norberto M Grzywacz
Journal:  Front Hum Neurosci       Date:  2021-05-10       Impact factor: 3.169

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.