Literature DB >> 25217808

Chaotic itinerancy and its roles in cognitive neurodynamics.

Ichiro Tsuda1.   

Abstract

Chaotic itinerancy is an autonomously excited trajectory through high-dimensional state space of cortical neural activity that causes the appearance of a temporal sequence of quasi-attractors. A quasi-attractor is a local region of weakly convergent flows that represent ordered activity, yet connected to divergent flows representing disordered, chaotic activity between the regions. In a cognitive neurodynamic aspect, quasi-attractors represent perceptions, thoughts and memories, chaotic trajectories between them with intelligent searches, such as history-dependent trial-and-error via exploration, and itinerancy with history-dependent sequences in thinking, speaking and writing.
Copyright © 2014 Elsevier Ltd. All rights reserved.

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Year:  2014        PMID: 25217808     DOI: 10.1016/j.conb.2014.08.011

Source DB:  PubMed          Journal:  Curr Opin Neurobiol        ISSN: 0959-4388            Impact factor:   6.627


  14 in total

1.  Itinerant complexity in networks of intrinsically bursting neurons.

Authors:  Siva Venkadesh; Ernest Barreto; Giorgio A Ascoli
Journal:  Chaos       Date:  2020-06       Impact factor: 3.642

2.  Reaction dynamics analysis of a reconstituted Escherichia coli protein translation system by computational modeling.

Authors:  Tomoaki Matsuura; Naoki Tanimura; Kazufumi Hosoda; Tetsuya Yomo; Yoshihiro Shimizu
Journal:  Proc Natl Acad Sci U S A       Date:  2017-02-06       Impact factor: 11.205

Review 3.  Itinerancy between attractor states in neural systems.

Authors:  Paul Miller
Journal:  Curr Opin Neurobiol       Date:  2016-06-16       Impact factor: 6.627

4.  The entropy of chaotic transitions of EEG phase growth in bipolar disorder with lithium carbonate.

Authors:  Rüştü Murat Demirer; Sermin Kesebir
Journal:  Sci Rep       Date:  2021-06-04       Impact factor: 4.379

5.  Iterative free-energy optimization for recurrent neural networks (INFERNO).

Authors:  Alexandre Pitti; Philippe Gaussier; Mathias Quoy
Journal:  PLoS One       Date:  2017-03-10       Impact factor: 3.240

Review 6.  Dynamical systems, attractors, and neural circuits.

Authors:  Paul Miller
Journal:  F1000Res       Date:  2016-05-24

7.  The influence of filtering and downsampling on the estimation of transfer entropy.

Authors:  Immo Weber; Esther Florin; Michael von Papen; Lars Timmermann
Journal:  PLoS One       Date:  2017-11-17       Impact factor: 3.240

8.  Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI.

Authors:  Georgia Koppe; Hazem Toutounji; Peter Kirsch; Stefanie Lis; Daniel Durstewitz
Journal:  PLoS Comput Biol       Date:  2019-08-21       Impact factor: 4.475

Review 9.  Dynamic Computation in Visual Thalamocortical Networks.

Authors:  Roy Moyal; Shimon Edelman
Journal:  Entropy (Basel)       Date:  2019-05-16       Impact factor: 2.524

10.  The brain dynamics of linguistic computation.

Authors:  Elliot Murphy
Journal:  Front Psychol       Date:  2015-10-13
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