Neural dynamics and brain function

The paper investigates neural oscillations during audiovisual sequence prediction, finding theta-band coupling and beta power patterns linked to prediction accuracy and cross-frequency interactions.

This study uses pharmaco-EEG to show diazepam alters alpha oscillation shape features in specific brain regions, supporting the neurophysiological relevance of waveform shape in non-invasive human recordings.

NEuRT, a BERT-based model, analyzes neuronal activity to classify Alzheimer's disease models by identifying group-specific features through attention maps.

This paper introduces a framework linking oscillatory and broadband activities to transient salient events in resting brain data, showing how they emerge from spectral properties and interactions.

This study shows infants detect visual regularities through neural entrainment, with greater occipital responses to deterministic patterns than random ones early in exposure.

This study shows that aperiodic exponents of brain field potentials vary with frequency range, highlighting the need for consistent frequency range selection in neuroscience research.

This study investigates how different temporal structures of item presentation affect brain oscillations during verbal working memory tasks, finding that alpha power is modulated by the presence of free intervals between stimuli.

This study shows that frontal theta synchronization via tACS improves the updating of statistical regularities by reducing erroneous anticipations and enabling flexible predictions.

This study shows that human cortical dynamics are influenced by both local geometry and network topology, with transmodal regions showing additional dependence on microstructural and connectivity factors.

This study uses AR modeling to detect behavioral oscillations in theta band across two datasets, finding reliable oscillations but no consistent frequency shift with task difficulty.

This study investigates brain-body dynamics across cognitive states and finds they are stable, with strong bottom-up feedback and narrative entrainment.

The paper proposes a function for thalamocortical loops in evidence accumulation and decision-making, showing they can operate in integrating or amplifying regimes based on cortical input.

The paper introduces two information-theoretic measures, ISSI and Iloc, to quantify neuronal sensitivity in neural systems, showing they outperform Fisher information in realistic conditions.

The paper investigates working memory maintenance by analyzing neural activity in macaques, finding that asynchronous firing and off-states contribute to memory persistence.

This study finds that the aperiodic exponent in resting-state EEG moderates age-related memory decline in older adults, with higher exponents associated with better memory performance.

The paper shows that retinotopy in macaque V1 shapes neural population activity into loop-like manifolds, confirmed by both experimental data and a minimal neural response model.

DendroTweaks provides an interactive toolbox for exploring and validating detailed biophysical neuronal models with active dendrites.

The paper tests the Spatial Computing hypothesis by analyzing alpha/beta oscillations in non-human primates, finding they organize task-related information and constrain sensory expression in space.

The paper shows neuronal classification is dynamic, not static, by demonstrating input patterns significantly influence functional classification in mice cortical neurons.

The paper introduces LIVN, an open-source simulation environment for developing and testing machine learning models to control in vitro neural networks using synthetic data with ground truth.

This study demonstrates that dissociated hippocampal cells can self-organize into network dynamics similar to in vivo cognitive cell assemblies, showing resilience and response to perturbations.

This study examines alpha-theta cross-frequency dynamics during working memory and finds reduced inter-areal connectivity despite increased 2:1 frequency ratios.

This study investigates how hippocampocortical circuits support probabilistic reversal learning by analyzing neural oscillations and spiking activity in rats.

The paper shows that cortical spiking variability arises from weak synchrony rather than intrinsic properties, with synchrony dynamics shifting in response to sensory input.

The paper shows that strengthening connections between left and right motor cortex increases information transfer in specific EEG frequency bands in right-handed individuals.

The paper identifies how prior expectations modulate brain activity during decision-making and shows that a few-parameter model captures these dynamics across the brain.

The paper links age-related cellular and synaptic changes in human cortical microcircuits to impaired brain function and EEG biomarkers using computational models and machine learning.

This study shows that short excitatory projections in neural networks increase neural correlations, promote bursting activity, and enable modular functional organization.

This study shows that the spectral slope of population activity cannot reliably predict excitation-inhibition balance in neural networks.

The paper identifies criticality signatures in cortical networks during synchronization onset, showing both intra- and inter-hemispheric coordination patterns.