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Chunking dynamics: heteroclinics in mind
Recent results of imaging technologies and non-linear dynamics make possible to relate the structure and dynamics of functional brain networks to different mental tasks and to build theoretical models for the description and prediction of cognitive activity. Such models are non-linear dynamical desc...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3954027/ https://www.ncbi.nlm.nih.gov/pubmed/24672469 http://dx.doi.org/10.3389/fncom.2014.00022 |
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author | Rabinovich, Mikhail I. Varona, Pablo Tristan, Irma Afraimovich, Valentin S. |
author_facet | Rabinovich, Mikhail I. Varona, Pablo Tristan, Irma Afraimovich, Valentin S. |
author_sort | Rabinovich, Mikhail I. |
collection | PubMed |
description | Recent results of imaging technologies and non-linear dynamics make possible to relate the structure and dynamics of functional brain networks to different mental tasks and to build theoretical models for the description and prediction of cognitive activity. Such models are non-linear dynamical descriptions of the interaction of the core components—brain modes—participating in a specific mental function. The dynamical images of different mental processes depend on their temporal features. The dynamics of many cognitive functions are transient. They are often observed as a chain of sequentially changing metastable states. A stable heteroclinic channel (SHC) consisting of a chain of saddles—metastable states—connected by unstable separatrices is a mathematical image for robust transients. In this paper we focus on hierarchical chunking dynamics that can represent several forms of transient cognitive activity. Chunking is a dynamical phenomenon that nature uses to perform information processing of long sequences by dividing them in shorter information items. Chunking, for example, makes more efficient the use of short-term memory by breaking up long strings of information (like in language where one can see the separation of a novel on chapters, paragraphs, sentences, and finally words). Chunking is important in many processes of perception, learning, and cognition in humans and animals. Based on anatomical information about the hierarchical organization of functional brain networks, we propose a cognitive network architecture that hierarchically chunks and super-chunks switching sequences of metastable states produced by winnerless competitive heteroclinic dynamics. |
format | Online Article Text |
id | pubmed-3954027 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-39540272014-03-26 Chunking dynamics: heteroclinics in mind Rabinovich, Mikhail I. Varona, Pablo Tristan, Irma Afraimovich, Valentin S. Front Comput Neurosci Neuroscience Recent results of imaging technologies and non-linear dynamics make possible to relate the structure and dynamics of functional brain networks to different mental tasks and to build theoretical models for the description and prediction of cognitive activity. Such models are non-linear dynamical descriptions of the interaction of the core components—brain modes—participating in a specific mental function. The dynamical images of different mental processes depend on their temporal features. The dynamics of many cognitive functions are transient. They are often observed as a chain of sequentially changing metastable states. A stable heteroclinic channel (SHC) consisting of a chain of saddles—metastable states—connected by unstable separatrices is a mathematical image for robust transients. In this paper we focus on hierarchical chunking dynamics that can represent several forms of transient cognitive activity. Chunking is a dynamical phenomenon that nature uses to perform information processing of long sequences by dividing them in shorter information items. Chunking, for example, makes more efficient the use of short-term memory by breaking up long strings of information (like in language where one can see the separation of a novel on chapters, paragraphs, sentences, and finally words). Chunking is important in many processes of perception, learning, and cognition in humans and animals. Based on anatomical information about the hierarchical organization of functional brain networks, we propose a cognitive network architecture that hierarchically chunks and super-chunks switching sequences of metastable states produced by winnerless competitive heteroclinic dynamics. Frontiers Media S.A. 2014-03-14 /pmc/articles/PMC3954027/ /pubmed/24672469 http://dx.doi.org/10.3389/fncom.2014.00022 Text en Copyright © 2014 Rabinovich, Varona, Tristan and Afraimovich. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Rabinovich, Mikhail I. Varona, Pablo Tristan, Irma Afraimovich, Valentin S. Chunking dynamics: heteroclinics in mind |
title | Chunking dynamics: heteroclinics in mind |
title_full | Chunking dynamics: heteroclinics in mind |
title_fullStr | Chunking dynamics: heteroclinics in mind |
title_full_unstemmed | Chunking dynamics: heteroclinics in mind |
title_short | Chunking dynamics: heteroclinics in mind |
title_sort | chunking dynamics: heteroclinics in mind |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3954027/ https://www.ncbi.nlm.nih.gov/pubmed/24672469 http://dx.doi.org/10.3389/fncom.2014.00022 |
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