The goal of this study was to evaluate different neurodynamic representations for their ability to describe the interactions of team members with each other and with the changing task. Electroencephalography (EEG) data streams were collected from six crew members of a submarine piloting and navigation team while they performed a required training simulation. A representation of neurodynamic organization was first generated by creating symbols every second that showed the EEG power levels of each crew member. The second-by-second expression of these symbols continuously varied with the changing task, and the magnitude, duration and frequency of these variations could be quantitated using a moving window of Shannon entropy over the symbol stream. These changes in neurodynamic organization (i.e. entropy) were seen in the alpha, beta and gamma EEG frequency bands. A representation of team members' synchrony was created by measuring the mutual information in the EEG power levels for fourteen dyad combinations. Mutual information was present in the gamma EEG band, and elevated levels were distributed throughout the task. These discrete periods of synchrony were poorly correlated at zero lag with either changes in the team's neurodynamic organization, or speech patterns.
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Cogn Neurodyn
December 2025
Shanghai University, Shanghai, China.
Neurodynamic observations indicate that the cerebral cortex evolved by self-organizing into functional networks, These networks, or distributed clusters of regions, display various degrees of attention maps based on input. Traditionally, the study of network self-organization relies predominantly on static data, overlooking temporal information in dynamic neuromorphic data. This paper proposes Temporal Self-Organizing (TSO) method for neuromorphic data processing using a spiking neural network.
View Article and Find Full Text PDFNeural Netw
December 2024
School of Mathematics and Statistics, Yili Normal University, Yining 835000, China.
In this paper, a recurrent neural network is proposed for distributed nonconvex optimization subject to globally coupled (in)equality constraints and local bound constraints. Two distributed optimization models, including a resource allocation problem and a consensus-constrained optimization problem, are established, where the objective functions are not necessarily convex, or the constraints do not guarantee a convex feasible set. To handle the nonconvexity, an augmented Lagrangian function is designed, based on which a recurrent neural network is developed for solving the optimization models in a distributed manner, and the convergence to a local optimal solution is proven.
View Article and Find Full Text PDFCureus
November 2024
Internal Medicine, Mahatma Gandhi Institute of Medical Sciences, Wardha, IND.
Introduction: Yoga practices emphasize spinal energy's role in physical, mental, and spiritual well-being, suggesting specific techniques that can enhance energy flow along the spine. Modern research aims to validate these claims and understand the mechanisms behind these effects, potentially integrating them into contemporary healthcare models. This study explores the relationship between yoga breathing techniques, spinal energy dynamics, and health based on yoga philosophy and bioenergetics.
View Article and Find Full Text PDFMedicina (Kaunas)
November 2024
Departamento de Fisioterapia, Facultad de Medicina, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Spain.
Neural Netw
October 2023
University of Rijeka, Rijeka, Croatia.
Accentuation has been proposed as a general principle of perceptual organization. Here, we have developed a neurodynamic architecture to explain how accentuation affects boundary segmentation and shape perception. The model consists of bottom-up and top-down pathways.
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