Neural dynamics and brain function

Temporal persistence and structural organization of neuronal avalanche dynamics

The paper analyzes neuronal avalanche dynamics in resting-state MEG data to show temporal persistence and structural organization in brain activity.

Prioritization in working memory reduces interference via beta band-linked stimulus transformations

This paper shows that prioritization in working memory reduces interference by transforming the unprioritized item into an 'unprioritized' state, linked to low-beta frequency oscillations.

BRAINCELL modelling platform for stochastic nanoscale organisation and dynamic extracellular signalling among neurons and glia

NEURON-based platform that stochastically adds dendritic spines, astrocyte nanoprocesses and a dynamic extracellular space; example simulations show spines widen input-output range and myelin Kir4.1 sustains high-frequency conduction.

Cycle-by-cycle respiration waveforms are coupled with the shape of neural oscillations

The paper examines the coupling between respiration and neural oscillations by analyzing breath-by-breath waveform shapes in human brain recordings.

Synaptic-state-dependent plasticity routes learning signals in recurrent circuits

The paper introduces a plasticity-switching recurrent neural network (psRNN) that models synaptic state-dependent plasticity and shows it improves learning efficiency in cognitive tasks.

Polysynaptic signal propagation in networked neural masses

This study examines polysynaptic signal propagation in neural mass models across different dynamical regimes and benchmarks results against empirical data.

Distinct theta mechanisms mediate target enhancement and distractor suppression in the primate prefrontal--V4 network

The paper investigates how theta mechanisms in the primate prefrontal-V4 network mediate target enhancement and distractor suppression during spatial attention.

Inferring When to Act from Temporal Regularities

The paper develops a virtual reality task to study how learned temporal regularities influence decision-making about when to act, showing behavioral and neural adaptations.

Transient gamma events delineate somatosensory modality in S1

The paper investigates differences in gamma band activity in S1 between noxious and innocuous stimuli in mice, finding more transient gamma events after noxious stimulation.

Visual experience drives functional reorganization of cortical GABAergic circuits

The paper shows that visual experience enhances SST interneuron activity and suppresses VIP interneurons, leading to increased excitatory neuron selectivity and network synchrony.

Excitation-inhibition balance dynamics across brain networks during naturalistic behavior

The paper investigates excitation-inhibition balance dynamics in brain networks during naturalistic behavior in rats, finding coordinated E-I dynamics and transient imbalances linked to behavioral changes.

The functional significance of EEG phase synchronization networks during information integration of left and right visual fields

This study identifies a broadband EEG phase synchronization network supporting interhemispheric visual integration in healthy adults during a visual task.

Connectome-constrained modeling identifies neurons and synapses that sustain spontaneous activity in Drosophila

The paper uses a connectome-constrained model to identify neurons and synapses sustaining spontaneous activity in Drosophila.

Information Flow and Traveling Waves show distinct cortical trajectories

The paper introduces DIFF, a method to map directed information flow in cortical networks, and shows it reveals distinct trajectories from traveling waves in simulations and electrophysiological recordings.

Shared Representation Discovery for Multi-Subject Neural Data Analysis

This paper introduces ShaReD, a method for analyzing multi-subject neural data without requiring temporal or anatomical alignment, and demonstrates its effectiveness on synthetic and animal data.

Self-regulation of neuronal activity in prefrontal cortex

This study uses a brain-computer interface to show animals can regulate prefrontal cortex activity through real-time neurofeedback, reducing neural fluctuations and linking this to arousal regulation.

Cognitive exertion reshapes resting-state EEG markers across the adult lifespan

The paper shows that cognitive tasks alter resting-state EEG markers like iAPF and aperiodic exponent across adult ages, with age-dependent changes in aperiodic shifts.

State-dependent structured sparsification of cortical signal-transmission networks enhances visual coding

The paper investigates how behavioral states reconfigure neuronal networks in mice, finding that locomotion leads to structured sparsification of signal transmission, enhancing sensory coding efficiency.

The Movie After-Effect: widespread adaptation of human cortex following naturalistic sensory experience

The paper shows that watching movies causes widespread adaptation in human cortex, with neurons adjusting their activity levels during rest periods after viewing.

Responsiveness of cerebral cortex to hippocampal inputs depends on brain region and brain state

The paper investigates how gamma oscillations affect cortical responsiveness to hippocampal inputs, finding that low-amplitude gamma enhances communication in prefrontal cortex but not retrosplenial cortex.

Quantifying information stored in synaptic connections rather than in firing activities of neural networks

This paper develops a theoretical framework to quantify information stored in synaptic connections of neural networks, showing synergistic interactions among synapses.

Geometric constraints and cognitive inputs jointly shape emergent brain dynamics and topology

This study shows that bioRNNs, which incorporate brain geometry, better predict fMRI activity and replicate brain hierarchy than other RNNs.

On the Optimal Temporal Resolution for Information Representation in Neural Activity: A Theoretical Analysis

The paper develops a theoretical framework to determine optimal temporal scales for neural information representation based on signal and noise autocorrelation dynamics.

Electrophysiological response geometries reveal shared and protocol-specific cell-type organization

The paper examines how different electrophysiological protocols reveal distinct but partially shared organizational structures of cell-type responses in mouse visual cortex.

Brain dynamics exhibit scale-dependent reversibility during consciousness transitions

This study examines consciousness transitions in rats using multiple modalities, finding scale-dependent reversibility in local dynamics but distinct global patterns during loss and recovery of consciousness.

Inferred Structure of a Neuronal Circuit for Economic Decisions

The paper infers the structure of a neuronal circuit in the orbitofrontal cortex involved in economic decisions using network analysis and finds a reproducible, state-dependent network structure.

State-dependent aperiodic EEG dynamics track cortical network reorganization in chronic epilepsy

This study examines how aperiodic EEG dynamics change in chronic epilepsy, showing they reflect both network reorganization and waveform shape effects across different brain states.

State-dependent, frequency-specific modulation of electrocortical traveling waves by anesthesia

This study examines how anesthesia affects cortical traveling waves across different states, finding frequency-specific changes and a paradoxical state resembling wakefulness during deep anesthesia.

Sensorimotor remapping drives task specialization in prefrontal cortex

The paper investigates how sensorimotor remapping affects modularity in the medial prefrontal cortex across different task paradigms, finding increased modularity with full remapping and reduced modularity with partial remapping.

Spatial Attention in the Moving Brain: Dissociable Roles of Neural Alpha Oscillations and Head Rotation

This study examines how neural alpha oscillations and head movements contribute to auditory spatial attention in humans, finding that both mechanisms work together to improve task accuracy.