Neural dynamics and brain function

This study examines how critical dynamics in the hippocampus shift during learning and sleep to support cognitive functions.

This study examines resting-state brain oscillations in children aged 2 to 68 months, finding that aperiodic measures mature nonlinearly and predict developmental outcomes.

The paper investigates how spatially targeted inhibitory rhythms affect neuronal integration in a layer 5 pyramidal neuron model.

The paper introduces a three-factor learning rule to model how global signals guide local synaptic updates for mismatch computation in sensory areas.

This paper develops a closed-loop biological neural network system that learns and generates temporal patterns through feedback control, demonstrating adaptability and reduced synchronization.

This study shows that instantaneous beta frequency in humans dynamically controls self-generated timing by predicting duration production.

The paper shows that baseline activity of V1 interneurons mediates the relationship between pupil-linked arousal and engaged behavioral state in mice and humans.

The paper introduces CHOIR, a method to analyze higher-order interactions in neuronal recordings, revealing circuit motifs associated with different brain states and activity patterns in mice.

The paper develops a biophysically grounded model of HVC network dynamics to explain how inhibition structures sparse sequential activity in songbirds.

This paper introduces SMDS, a model that accounts for representational drift in neural data, outperforming LDS in log-likelihood and requiring fewer latent dimensions.

The paper shows that switching between tonic and burst firing in neural networks creates a burst-induced attractor in synaptic weight dynamics, which stabilizes synapses and supports memory consolidation.

The paper derives probabilistic rules for combining excitatory and inhibitory inputs in neural circuits to explain how neurons respond to stimuli and modulate their input-output properties.

The paper introduces a new quality metric for electrophysiological recordings that adapts to varying refractory period durations across brain regions and species.

This study investigates how parietal cortex inactivation affects intrinsic neural timescales in the frontal eye field, revealing distinct neuronal dynamics and functional roles.

The paper identifies a whole-brain transition-prone state that predicts spontaneous behavioral switches in mice using functional ultrasound imaging and optogenetics.

The paper identifies a brain-wide hemodynamic wave associated with arousal that occurs independently of locus coeruleus noradrenergic activity.

This study shows that education moderates the relationship between age-related changes in EEG aperiodic components and cognitive performance, with different patterns observed in individuals with varying education levels.

The paper develops an efficient computing theory to explain optimal representations in prefrontal cortex working memory tasks, showing compositional and contextual codes as extremes of a spectrum determined by task structure.

The paper proposes a spatiotemporal cortical field model to simulate complex spiral traveling waves and their dynamics in local cortical circuits.

This paper connects cortical circuit models with low-dimensional population dynamics using a recurrent spiking network model and demonstrates a method to retrieve circuit structure from recordings.

The paper shows that neuronal parameter diversity is structured by energy-information trade-offs, forming a Pareto front in parameter space.

This study shows how rapid learning in mice involves structured changes in motor cortex connectivity, rerouting preparatory activity to control a conditioned neuron.

This study shows that inhibitory feedback combined with predictive learning allows neural networks to efficiently anticipate and classify multiple spike sequences.

The paper introduces a dynamical manifold theory of cognitive difficulty using excitable neural networks, showing difficulty emerges as a multidimensional structure rather than a single scalar value.

The paper simulates sensory signal propagation in the Drosophila connectome to study modality-specific cascades and neuron classification based on dynamic response profiles.

The paper quantifies conduction velocity changes along axonal branches and finds a right-skewed distribution of velocity ratios that is invariant across branch lengths and neuron positions.

This study uses mouse forebrain organoids to show how variations in cellular composition influence the self-organization of neural circuits, revealing intrinsic principles of forebrain circuit assembly.

The paper introduces a simplified model of intrinsically bursting neurons that balances biological realism with practical usability for circuit simulations.

The paper uses data-derived agents to study neural dynamics in mouse cortex, revealing chaotic attractors that support robust and adaptable behavior.

The paper investigates whether hippocampal memory reactivation ensures overt recall, finding that alpha oscillations predict recall rather than reactivation alone.