Cognitive Neurodynamics
October 20, 2017
D. M. Mateos, R. Guevara Erra, R. Wennberg et al.
124 citations
Brain signals are most complex when people are fully awake and alert, and complexity decreases during sleep and epileptic seizures. Researchers analyzed electroencephalography (EEG), intracranial EEG, and magnetoencephalography recordings from subjects during resting wakefulness, different sleep stages, and seizures. They used permutation entropy and permutation Lempel-Ziv complexity to measure signal complexity. Complexity-versus-entropy graphs showed that both measures were highest during wakefulness and fell during states with reduced awareness. These patterns held across all three recording types. The authors suggest that studying the structure of cognition through complexity frameworks can reveal brain dynamics underlying normal, altered, and pathological states of consciousness.
Cognitive Neurodynamics
December 1, 2025
Bingxin Lin, Baoshun Guo, Lingyun Zhuang et al.
During flow, a state of deep immersion in an activity, the brain shows higher theta power, moderate alpha power, and lower beta power compared to non-flow states, suggesting a focused yet effortless neural pattern. Machine learning (Lasso regression) predicted individuals' subjective flow scores from EEG data with a correlation of 0.571, indicating that flow can be objectively quantified from neural oscillations.
Cognitive Neurodynamics
August 1, 2024
Wiesław L Galus
A new graphical, structural, and functional model of the embodied mind is presented. Adhering to a physicalistic and reductionist approach, it resolves the apparent contradiction between the causal closure of the physical realm and the common-sense belief that the mental realm influences physical behavior. The model substantiates mind-brain identity theory and explains its neural foundation. Consciousness is viewed as both an epiphenomenon and causally potent, operating through distinct brain processes. Emphasis is placed on qualia and emotions, with their phenomenal nature explained via the perceptual theory of emotions. The model shows how autonomous agents deliberate on action scenarios and consciously select optimal ones based on knowledge, motivations, preferences, and emotions.
Cognitive Neurodynamics
December 1, 2021
Yue Yuan, Xiaochuan Pan, Rubin Wang
A theoretical model coupling the default mode network (DMN) and working memory network (WMN) simulates neural dynamics during encoding, maintenance, and retrieval phases. AMPA channels produce synchronous oscillations that shift oscillation patterns in both networks. Different NMDA conductance between networks generates multiple neural activity modes, potentially switching network states across memory phases. The number of sequentially memorized stimuli relates to energy consumption determined by internal parameters, with the DMN contributing to more stable working memory. Different memory phases correspond to different functional connections between DMN and WMN, with coupling strengths differing in phase synchronization. Phase synchronization characteristics of contained energy match observed negative and positive correlations between networks from fMRI experiments.
Cognitive Neurodynamics
June 10, 2020
C. M. Signorelli, Daniel Meling
A theoretical paper introduces new concepts—closure, compositionality, biobranes, and autobranes—to model consciousness in a non-reductionist way. It argues that consciousness co-arises with the non-trivial composition of biological closure, where conscious processes generate closed activity at various levels and are supported by biobranes and autobranes. This approach aims to integrate biological and phenomenological perspectives, offering a new framework for a science of consciousness. Future work will develop experimental definitions and computational simulations to characterize these dynamical biobranes.