Chunking dynamics: heteroclinics in mind

• Published in: Frontiers in computational neuroscience Vol. 8; p. 22
• Main Authors: Rabinovich, Mikhail I, Varona, Pablo, Tristan, Irma, Afraimovich, Valentin S
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 14-03-2014; Frontiers Media S.A
• Subjects: Brain architecture; chunking and superchunking; Cognition & reasoning; Cognitive ability; cognitive dynamics; Functional morphology; Hierarchical sequences; Information processing; low dimensionality of brain activity; Mathematical models; Neuroimaging; Neuroscience; Nonlinear systems; Short term memory; Stable heteroclinic channel; Temporal variations; Transient Dynamics; stable heteroclinic channel; cognition modeling principles; low dimensionality of brain activity; hierarchical sequences; transient dynamics; chunking and superchunking; cognitive dynamics
• ISSN: 1662-5188; 1662-5188
• DOI: 10.3389/fncom.2014.00022

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.