Neural dynamics and brain function

The paper investigates age-dependent sex differences in EEG oscillations, finding robust sex differences in older adults but not younger adults, and links these patterns to lifestyle factors.

This paper develops a model-based approach to detect traveling waves in human visual cortex using MEG-EEG, showing improved accuracy when modeling stimulus-induced traveling waves.

The paper introduces a Hebbian-like Kuramoto model to simulate brain wave dynamics and shows it can reproduce dominant wave activity in human brain functional MRI signals.

The paper investigates how cortical stimulation engages subcortical circuits in mice and humans, showing similar evoked potentials and thalamic contributions across species.

MINT is a toolbox for analyzing multivariate neural information coding and transmission using information theory and dimensionality reduction, applied to simulated and real neural data.

The paper evaluates the kernel method for predicting electric brain signals from population firing rates, showing its accuracy depends on kernel heterogeneity and spike-train correlations.

This paper systematically compares four theoretical models of alpha rhythm generation in neural circuits, identifying common elements and differences, and classifying two dynamical regimes for alpha rhythms.

The paper investigates the role of δ subunit-containing GABA-A receptors on parvalbumin positive neurons in shaping electrocortical activity during sleep states, finding altered EEG patterns in their absence.

The paper shows burst firing in neural populations creates invariant representations of natural communication signals, demonstrated through recordings and computational modeling.

The paper identifies multiplexed subspace networks in the brain that allow regions to interact with multiple networks simultaneously, supporting cognitive flexibility.

The paper tests the CTC hypothesis by showing that a third of striatal neurons exhibit phase-dependent excitability in the delta frequency band.

This paper constructs a biophysical model of perceptual awareness in mice that explains neural and behavioral signatures and predicts electrophysiological responses.

The paper examines how aperiodic EEG activity relates to verbal fluency performance across adulthood, finding that smaller offsets correlate with poorer performance and greater aperiodic activity correlates with better performance.

This paper extends mathematical models to study how microglial ensheathment affects synaptic communication and network dynamics, showing it can switch off connections and lead to post-anesthesia hyperactivity in mice.

The paper measures the electric field from annihilating action potentials and shows they induce ephaptic coupling between neurons.

The paper demonstrates that cortical circuits can learn and spontaneously replay temporal patterns through asymmetric connectivity and prediction mechanisms.

This paper applies a corticothalamic model to analyze sleep EEG data from multiple datasets, revealing consistent physiological patterns and drug-related effects during sleep stages.

The paper identifies consistent hierarchical timescales of single neurons across mice, macaques, and humans, with species-specific variations in certain brain regions.

This paper identifies a mechanism for reliable neural information processing through neural heterogeneity, showing it disrupts spatiotemporal patterns and aligns network activity with inputs.

This paper introduces an interpretable deep learning method for deconvolving neural signals, demonstrating its application in analyzing brain activity across different regions and recording modalities.

The paper shows that neurons in the lateral intraparietal area encode confidence in decision accuracy through population-level variability, despite individual neurons showing no correlation with accuracy.

This paper models EEG activity under xenon and nitrous oxide as alpha band relaxation oscillators, finding increased damping correlates with reduced alpha power and 1/f scaling.

The paper studies theta-oscillons in behaving rats, showing they resemble theta-waves in key characteristics and exhibit behavior-attuned properties.

This study tests if recurrent inhibitory interneuron connectivity can generate synchrony in a biophysical dentate gyrus model and finds it insufficient.

This study investigates how neural dynamics in V4 correlate with false alarms in a visual change-detection task, finding that stability in latent dynamics affects perceptual behavior and decision-making.

The paper shows that auditory processing depends on neural oscillation phase when processing task-irrelevant tones, unlike visual processing, and demonstrates this through EEG data from human participants.

This study examines theta and alpha oscillatory episodes during spatial navigation in humans using single-trial analysis in a virtual reality environment, finding their relation to task difficulty and navigational decisions.

This study shows that reducing h-current in cortical networks increases neuronal responsiveness and alters slow oscillation dynamics by extending both Up and Down states and reducing oscillation frequency.

The paper investigates how electrical coupling and the H-current interact to influence coincidence detection in inhibitory synaptic inputs, with implications for neural network dynamics and motor coordination.

This study identifies theta and beta oscillations across spatial scales as correlates of distractor anticipation during working memory maintenance in monkeys.