Neural dynamics and brain function

This study identifies local thalamic interneurons as critical for regulating spindles and sleep-dependent learning by modulating thalamocortical activity.

This paper introduces DcKONN, a deep neural network inspired by biological oscillatory dynamics, and shows it achieves competitive classification accuracy on EEG signals.

This study uses dynamical models to show that aging brains show shifts in parameters indicating a transition to a supercritical regime, suggesting altered brain dynamics.

The paper shows that an optimal ratio of inhibitory neurons is necessary for developing synchronized network activity in mouse cortical cultures.

The paper demonstrates that a single arousal measurement can reconstruct spatiotemporal brain dynamics across multiple modalities in awake mice.

The paper uses a biophysical model to show that gain-controlled reconfiguration of long-range neural coupling explains visibility-dependent spatiotemporal coding dynamics in EEG data.

The paper shows that connectivity asymmetry in brain networks supports higher working-memory capacity, while increased reciprocity reduces it, using computational models of recurrent neural networks.

This study shows that detrending neural spectra conflates rhythmic and arrhythmic brain activity, while specparam provides accurate separation of these components.

The paper investigates how reversal probability is represented in prefrontal cortex neurons and recurrent neural networks during probabilistic reversal learning, finding stable initial states that drive non-stationary dynamics.

The paper uses interpretable machine learning to derive dynamical equations from seizure data, revealing a chaotic attractor mechanism in thalamocortical circuits that drives absence seizures.

The paper investigates the temporal dynamics of retroperception, finding that conscious access to a stimulus decays with damped theta-band oscillations.

The paper investigates how network dynamics differ between cortical regions with distinct activity timescales, showing that long-timescale areas sustain input over longer periods through specific neuronal recruitment patterns.

This paper predicts EEG neural rhythms from fMRI data across sleep and wakefulness using machine learning, identifying large-scale network patterns associated with alpha and delta rhythms.

The paper shows thalamocortical axons target specific dendritic domains in pyramidal tract neurons to enable calcium-dependent spiking and modulate cortical output through sensory input.

The paper uses classification scores of postsynaptic spike counts to map indistinguishability domains of neural microcircuit motifs based on synaptic parameters.

This paper develops a framework for simulating neuron-astrocyte interactions in large-scale neural networks, showing astrocytes drive synchronization in local neuronal groups.

This study shows aging reduces functional connectivity in mouse V1 and CA1 regions, affecting synchrony in specific frequency bands without impairing single-neuron activity.

This study shows that pulvinar bursts modulate attention and improve behavioral performance in macaques.

The paper shows that traveling waves in the insula correlate with memory performance during a spatial episodic memory task.

The paper introduces a two-parameter model based on neural signals to predict human lifespan and explains age-related changes in fractal dimension and gamma oscillations.

This paper investigates frequency-dependent coupling in electrically coupled networks, analyzing how intrinsic cellular properties and nonlinearities shape coupling coefficients in response to oscillatory inputs.

This study examines how Nrxn1 loss-of-function affects prior utilization and neural dynamics in a mouse model of autism, revealing impaired decision-making and altered cortical activity.

This study shows how weak neural sequences can overcome competition by increasing connectivity or cooperating with other sequences, affecting their speed and reactivation.

The paper investigates neural resonance in the infant visual system, finding a 4Hz theta rhythm independent of stimulation frequency, contrasting with the 10Hz alpha rhythm in adults.

The paper shows that ramping neural activity in visual and parietal cortex during passive behavior results from stimulus-evoked dynamics, not predictive processing.

The paper presents a computational model of predictive coding in the cortex that incorporates biologically plausible neuron types and connectivity, showing how they generate oscillatory patterns and handle prediction errors.

This study investigates neural dynamics in Visual Working Memory by comparing repeated and non-repeated item retrieval, finding that repeated items are prioritized and show distinct EEG patterns.

This study demonstrates that inhibitory theta phase locking in interneurons directly controls seizure susceptibility using a closed-loop optogenetic system.

This paper introduces a minimal mean-field neural circuit model that explains flexible perceptual decisions by integrating sensory evidence and action selection, replicating parietal cortical activity and decision dynamics.

This study shows that ACC inputs to the claustrum change from excitatory to inhibitory with age, linked to parvalbumin interneuron maturation in mice.