The proposed methods are directed at unification of conducting and assessing neurodynamic properties of the higher nervous activity of a human that are related to the processing of visual information of various complexity levels. It should be considered that the conducting of examinations in maximum close conditions of the same tests and assessment criteria will increase the possibilities and the value of the analysis of various experimental materials.
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Cogn Neurodyn
December 2024
Basque Center for Applied Mathematics, Bilbao, Spain.
Computational modeling of neurodynamical systems often deploys neural networks and symbolic dynamics. One particular way for combining these approaches within a framework called leads to neural automata. Specifically, neural automata result from the assignment of symbols and symbol strings to numbers, known as Gödel encoding.
View Article and Find Full Text PDFBiol Psychiatry Cogn Neurosci Neuroimaging
November 2024
Edmond Safra Brain Research Center, Faculty of Education, University of Haifa, Haifa, 3498838, Israel b Department of Psychosomatic Medicine and Psychotherapy, Medical Center; Integrated Brain and Behavior Research Center (IBBRC), University of Haifa, Haifa, Israel; Department of Counseling and Human Development, Faculty of Education, University of Haifa, Haifa, Israel; Department of Learning and Instructional Sciences, Faculty of Education, University of Haifa, Haifa, Israel. Electronic address:
There is a renewed interest in taking phenomenology seriously in consciousness research, contemporary psychiatry, and neurocomputation. The neurophenomenology research program, pioneered by Varela (1996), rigorously examines subjective experience using first-person methodologies, inspired by phenomenology and contemplative practices. This review explores recent advancements in neurophenomenological approaches, particularly their application to meditation practices and potential clinical research translations.
View Article and Find Full Text PDFStability in recurrent neural models poses a significant challenge, particularly in developing biologically plausible neurodynamical models that can be seamlessly trained. Traditional cortical circuit models are notoriously difficult to train due to expansive nonlinearities in the dynamical system, leading to an optimization problem with nonlinear stability constraints that are difficult to impose. Conversely, recurrent neural networks (RNNs) excel in tasks involving sequential data but lack biological plausibility and interpretability.
View Article and Find Full Text PDFChaos
October 2024
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia 30303, USA.
Cognit Comput
December 2022
Johns Hopkins University Applied Physics Laboratory, Laurel, MD USA.
Artificial intelligence has not achieved defining features of biological intelligence despite models boasting more parameters than neurons in the human brain. In this perspective article, we synthesize historical approaches to understanding intelligent systems and argue that methodological and epistemic biases in these fields can be resolved by shifting away from cognitivist brain-as-computer theories and recognizing that brains exist within large, interdependent living systems. Integrating the dynamical systems view of cognition with the massive distributed feedback of perceptual control theory highlights a theoretical gap in our understanding of nonreductive neural mechanisms.
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