electrophysiology

This study examines auditory evoked potentials in Kabuki syndrome and Rubinstein-Taybi syndrome, finding differences from controls and correlations with cognitive aspects.

This paper proposes a lightweight, interpretable method for emotion recognition using EEG foundation models by analyzing attention maps, achieving competitive performance with fewer computations.

This paper introduces SPress, a method that enables high-speed, high-contrast voltage imaging of large neuronal populations in vivo with mesoscopic resolution.

This paper introduces Braincodec, a deep encoder that improves sound transmission via cortical stimulation, achieving high-fidelity sound reconstruction and matching non-implanted mice in sound discrimination.

The paper shows that a dual-band vibration burst improves anti-kindling in a spiking network model compared to single-frequency stimulation.

The paper shows that mice retain spatial memory in the hippocampus after long absences, with neural patterns persisting despite changes in individual neuron activity.

This study finds that a single session of mirror visual feedback does not significantly alter subsequent motor imagery-related neural activity.

The paper uses a bio-realistic model of mouse primary visual cortex to show that cortical oscillations follow a spectrolaminar motif, with gamma in superficial layers and alpha/beta in deep layers, driven by specific cell types and synaptic properties.

The paper shows curved hippocampal geometry masks theta oscillations through regional cancellation, while beta and gamma oscillations remain observable due to less coordinated generators.

This study shows that subquantal serotonin release during kiss-and-run exocytosis is due to fusion-pore closure, not vesicle depletion.

The paper investigates how neural circuits in the primate frontal cortex support compositional generalization by separating computations into spatial and temporal components and identifying biases when these components are misaligned.

The paper updates the Human Neocortical Neurosolver model to include biophysically realistic dynamics in human neurons, enabling better interpretation of EEG/MEG signals.

The paper introduces a framework combining a fine-tuned LLM and TRFs to isolate social context's influence on speech perception, showing social context improves EEG prediction.

LeadWeaver improves ECG diagnosis by modeling lead topology and uncertainty, outperforming methods that treat missing leads as zero-valued inputs.

The paper investigates whether traveling waves in attention networks reflect top-down attention signals or are a byproduct of sustained attention, finding they align with the intrinsic rhythms of engaged sensory systems.

This study shows that 40 Hz visual stimulation increases gamma-band functional connectivity between visual, parietal, and frontal regions, with the visual-parietal connection being most significant.

The paper examines feedback valence and prediction error in EEG responses related to anxiety, finding a symptom-linked association but limited computational specificity.

This study develops a method to culture human pediatric cardiac tissue slices for extended periods, enabling functional and pharmacological testing of cardiac physiology.

This study compares statistical and reward learning, finding shared attentional mechanisms but distinct computational dynamics in neural representations and learning rates.

The paper identifies differences in how two types of descending neurons in hoverflies encode visual information from small targets, revealing distinct pathways for target pursuit and threat detection.

The paper shows that simple nonlinearities in the visual cortex can explain complex interactions between visual, motor, and cognitive variables through additive input modulation.

This paper applies control theory to dendritic structures, showing how morphology and input organization affect resilience and energy cost, with implications for neuronal function.

The paper introduces the Spectral Complexity Index (SCI), a white-box measure of single-neuron computational complexity that outperforms existing methods in efficiency and interpretability.

This study compares age-specific pediatric EEG templates to adult templates and finds they better match individual MRI-based biomarkers in children up to 12 years old.

This study identifies distinct neural dynamics in ADHD children, showing atypical alpha oscillations for demand-related attention and aperiodic activity for distractor-related instability.

SafeLens uses nonlinear machine learning with statistical control to detect subtle, trial-varying neural dynamics in event-locked activity, outperforming traditional methods in sensitivity and specificity.

This study investigates how awareness and task relevance modulate mismatch responses to spoken words, finding that word-related MMN and P3 are only elicited when words are task-relevant and that attention is required for word MMRs.

This study investigates the relationship between cortical and corticospinal excitability thresholds along the motor cortex using TMS-EMG-EEG in 24 subjects, finding strong correlations and site-specific differences.

This paper introduces a method to cluster instantaneous phase-locking states using the Bures-Wasserstein distance, improving sensitivity to phase-lag size and spatial patterns in EEG data.