Literature DB >> 10724457

The labile brain. I. Neuronal transients and nonlinear coupling.

K J Friston1.   

Abstract

In this, the first of three papers, the nature of, and motivation for, neuronal transients is described in relation to characterizing brain dynamics. This paper deals with some basic aspects of neuronal dynamics, interactions, coupling and implicit neuronal codes. The second paper develops neuronal transients and nonlinear coupling in the context of dynamic instability and complexity, and suggests that instability or lability is necessary for adaptive self-organization. The final paper addresses the role of neuronal transients through information theory and the emergence of spatio-temporal receptive fields and functional specialization. By considering the brain as an ensemble of connected dynamic systems one can show that a sufficient description of neuronal dynamics comprises neuronal activity at a particular time and its recent history This history constitutes a neuronal transient. As such, transients represent a fundamental metric of neuronal interactions and, implicitly, a code employed in the functional integration of brain systems. The nature of transients, expressed conjointly in distinct neuronal populations, reflects the underlying coupling among populations. This coupling may be synchronous (and possibly oscillatory) or asynchronous. A critical distinction between synchronous and asynchronous coupling is that the former is essentially linear and the latter is nonlinear. The nonlinear nature of asynchronous coupling enables the rich, context-sensitive interactions that characterize real brain dynamics, suggesting that it plays a role in functional integration that may be as important as synchronous interactions. The distinction between linear and nonlinear coupling has fundamental implications for the analysis and characterization of neuronal interactions, most of which are predicated on linear (synchronous) coupling (e.g. cross-correlograms and coherence). Using neuromagnetic data it is shown that nonlinear (asynchronous) coupling is, in fact, more abundant and can be more significant than synchronous coupling.

Mesh:

Year:  2000        PMID: 10724457      PMCID: PMC1692735          DOI: 10.1098/rstb.2000.0560

Source DB:  PubMed          Journal:  Philos Trans R Soc Lond B Biol Sci        ISSN: 0962-8436            Impact factor:   6.237


  33 in total

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2.  Detecting dynamical interdependence and generalized synchrony through mutual prediction in a neural ensemble.

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Journal:  Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics       Date:  1996-12

3.  Neural dynamics in a model of the thalamocortical system. II. The role of neural synchrony tested through perturbations of spike timing.

Authors:  E D Lumer; G M Edelman; G Tononi
Journal:  Cereb Cortex       Date:  1997 Apr-May       Impact factor: 5.357

4.  A model for visual shape recognition.

Authors:  P M Milner
Journal:  Psychol Rev       Date:  1974-11       Impact factor: 8.934

5.  Temporal encoding of two-dimensional patterns by single units in primate inferior temporal cortex. I. Response characteristics.

Authors:  B J Richmond; L M Optican; M Podell; H Spitzer
Journal:  J Neurophysiol       Date:  1987-01       Impact factor: 2.714

6.  Stimulus-induced gamma oscillations: harmonics of alpha activity?

Authors:  E Jürgens; F Rösler; E Henninghausen; M Heil
Journal:  Neuroreport       Date:  1995-03-27       Impact factor: 1.837

Review 7.  Noise, neural codes and cortical organization.

Authors:  M N Shadlen; W T Newsome
Journal:  Curr Opin Neurobiol       Date:  1994-08       Impact factor: 6.627

8.  Dynamics of neuronal interactions in monkey cortex in relation to behavioural events.

Authors:  E Vaadia; I Haalman; M Abeles; H Bergman; Y Prut; H Slovin; A Aertsen
Journal:  Nature       Date:  1995-02-09       Impact factor: 49.962

9.  Attentional modulation of visual motion processing in cortical areas MT and MST.

Authors:  S Treue; J H Maunsell
Journal:  Nature       Date:  1996-08-08       Impact factor: 49.962

10.  Transient phase-locking of 40 Hz electrical oscillations in prefrontal and parietal human cortex reflects the process of conscious somatic perception.

Authors:  J E Desmedt; C Tomberg
Journal:  Neurosci Lett       Date:  1994-02-28       Impact factor: 3.046

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  67 in total

1.  Nonlinear phase desynchronization in human electroencephalographic data.

Authors:  Michael Breakspear
Journal:  Hum Brain Mapp       Date:  2002-03       Impact factor: 5.038

2.  Nonlinear synchronization in EEG and whole-head MEG recordings of healthy subjects.

Authors:  Cornelis J Stam; Michael Breakspear; Anne-Marie van Cappellen van Walsum; Bob W van Dijk
Journal:  Hum Brain Mapp       Date:  2003-06       Impact factor: 5.038

Review 3.  "Dynamic" connectivity in neural systems: theoretical and empirical considerations.

Authors:  Michael Breakspear
Journal:  Neuroinformatics       Date:  2004

4.  Enhancement of GABA-related signalling is associated with increase of functional connectivity in human cortex.

Authors:  Andrew A Fingelkurts; Alexander A Fingelkurts; Reetta Kivisaari; Eero Pekkonen; Risto J Ilmoniemi; Seppo Kähkönen
Journal:  Hum Brain Mapp       Date:  2004-05       Impact factor: 5.038

5.  Temporal delays among place cells determine the frequency of population theta oscillations in the hippocampus.

Authors:  Caroline Geisler; Kamran Diba; Eva Pastalkova; Kenji Mizuseki; Sebastien Royer; György Buzsáki
Journal:  Proc Natl Acad Sci U S A       Date:  2010-04-07       Impact factor: 11.205

6.  A phase synchrony measure for quantifying dynamic functional integration in the brain.

Authors:  Selin Aviyente; Edward M Bernat; Westley S Evans; Scott R Sponheim
Journal:  Hum Brain Mapp       Date:  2011-01       Impact factor: 5.038

Review 7.  On the role of general system theory for functional neuroimaging.

Authors:  Klaas Enno Stephan
Journal:  J Anat       Date:  2004-12       Impact factor: 2.610

Review 8.  A new approach to neuroimaging with magnetoencephalography.

Authors:  Arjan Hillebrand; Krish D Singh; Ian E Holliday; Paul L Furlong; Gareth R Barnes
Journal:  Hum Brain Mapp       Date:  2005-06       Impact factor: 5.038

Review 9.  Dynamics of a neural system with a multiscale architecture.

Authors:  Michael Breakspear; Cornelis J Stam
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2005-05-29       Impact factor: 6.237

10.  Consistent resting-state networks across healthy subjects.

Authors:  J S Damoiseaux; S A R B Rombouts; F Barkhof; P Scheltens; C J Stam; S M Smith; C F Beckmann
Journal:  Proc Natl Acad Sci U S A       Date:  2006-08-31       Impact factor: 11.205

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