Dynamical coherence patterns in neural field model at criticality

Termsaithong, Teerasit; Oku, Makito; Aihara, Kazuyuki

doi:10.1007/s10015-012-0020-x

Cited by 3 publications

(1 citation statement)

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“…It has to be stressed that Peng et al ( 1999 ) revealed that meditation has the effect of generating a surprising coherent behavior in HRV time series. The connection between coherence and criticality is a subject of great interest (Termsaithong et al, 2012 ) that we address herein using the subordination perspective (Sokolov, 2000 ; Thiel and Sokolov, 2017 ). The connection between meditation and coherence is a subject studied by many authors moving from the adoption of positive emotions (Bradley et al, 2010 ) to different forms of meditation (Peng et al, 1999 , 2004 ).…”

Section: Introductionmentioning

confidence: 99%

Meditation-Induced Coherence and Crucial Events

et al. 2018

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In this paper we emphasize that 1/f noise has two different origins, one compatible with Laplace determinism and one determined by unpredictable crucial events. The dynamics of heartbeats, manifest as heart rate variability (HRV) time series, are determined by the joint action of these different memory sources with meditation turning the Laplace memory into a strongly coherent process while exerting an action on the crucial events favoring the transition from the condition of ideal 1/f noise to the Gaussian basin of attraction. This theoretical development affords a method of statistical analysis that establishes a quantitative approach to the evaluation of the stress reduction realized by the practice of Chi meditation and Kundalini Yoga.

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Section: Introductionmentioning

confidence: 99%

Meditation-Induced Coherence and Crucial Events

et al. 2018

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Dynamical correlation patterns and corresponding community structure in neural spontaneous activity at criticality

Termsaithong

Aihara

2013

Cogn Neurodyn

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It has been considered that the state in the vicinity of a critical point, which is the point between ordered and disordered states, can underlie and facilitate information processing of the brain in various aspects. In this research, we numerically study the influence of criticality on one aspect of brain information processing, i.e., the community structure, which is an important characteristic of complex networks. We examine community structure of the functional connectivity in simulated brain spontaneous activity, which is based on dynamical correlations between neural activity patterns at different positions. The brain spontaneous activity is simulated by a neural field model whose parameter covers subcritical, critical, and supercritical regions. Then, the corresponding dynamical correlation patterns and community structure are compared. In the critical region, we found some distinctive properties, namely high correlation and correlation switching, high modularity and a low number of modules, high stability of the dynamical functional connectivity, and moderate flexibility of the community structure across temporal scales. We also discuss how these characteristics might improve information processing of the brain.

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