Neural dynamics and brain function

The paper develops a framework to predict neural network activity using connectome-constrained recurrent networks and shows that targeted neuron recordings can resolve ambiguities in network dynamics.

The paper uses topological data analysis to study neuronal assemblies in zebrafish optic tectum, finding low-rank structure and cholinergic modulation of their functional role.

The paper compares polychronous groups and cell ensembles in spiking neural networks under different STDP rules and network connectivity, showing discrepancies between anatomical and spike-pattern memory representations.

The paper evaluates 122 measures for analyzing synchrony, oscillations, and phase relationships in neuronal activity across synthetic and biological spike trains, highlighting method-specific biases and guiding their use.

This study uses MEG and machine learning to decode spatial attention in humans during different tasks, showing dynamic, task-dependent attentional patterns similar to those in non-human primates.

This paper introduces a continuous-time estimator of Transfer Entropy for spike trains, demonstrating its effectiveness in inferring effective neuronal networks and outperforming existing methods.

The paper investigates how distinct alpha brain networks influence perceptual decision-making by analyzing MEG data during a visual detection task, finding that the visual alpha network modulates sensitivity while the sensorimotor alpha network modulates criterion.

This study shows how neural oscillations modulate decision confidence, speed, and accuracy by altering synaptic time scales and oscillatory phase, challenging the speed-accuracy trade-off.

This paper uses mathematical modeling to show how synaptic short-term plasticity and cellular time scales shape neuronal frequency filters in a basic feedforward network.

The paper develops a computational model of olivocerebellar modules to study synaptic plasticity dynamics and shows how IO neuron oscillations and coupling regulate plasticity in Upbound and Downbound micromodules.

This paper develops a spiking neural network model of V1 layer 4 to study predictive coding, showing it can reproduce in vivo features and maintain E-I balance under varying input conditions.

The paper investigates neuronal dynamics during mental sound manipulation using intracranial recordings, finding changes in local excitability and inter-areal synchrony.

The paper uses computational modeling to show schizophrenia-associated changes in ion-channel expression reduce delta-band power in neural simulations.

The paper shows that working memory readout in the frontal eye field varies with theta rhythm phases, linked to performance in a visual memory task.

This study identifies disruptions in brain criticality and excitation-inhibition balance in Major Depressive Disorder using resting-state EEG data.

The paper shows that selective coupling and decoupling of neurons across brain networks prepare for skilled actions, with specific neural dynamics predicting action quality and performance.

The paper identifies collective variables in retinal ganglion cells that best predict future neural activity using an information bottleneck method.

This paper uses computational modeling to identify neural populations and connectivity patterns underlying the whisker-flick EEG waveform in rodents.

This study identifies synchrony-driven assemblies in ferret auditory cortex that reliably represent complex auditory stimuli through coordinated firing patterns.

The paper develops improved models for inferring latent neural states from calcium imaging data by integrating spiking and calcium dynamics directly into latent variable frameworks.

The paper identifies brain-wide subnetworks involved in social interactions between typical and autism model mice, revealing disrupted inter-brain synchrony in autism.

This paper introduces a spatial-temporal attention normalizing flow to model neuron synchronization in the insect antennal lobe, revealing interactions between cell types and odorant mixture dynamics.

This study shows that pre-stimulus alpha power in the visual cortex enhances visual memory performance by increasing initial stimulus availability without affecting memory decay.

The paper investigates how intrinsic neural timescales relate to event-related activity using computational modeling and MEG data, finding that intracolumnar connections are critical for both resting state and task-related responses.

The paper shows that spontaneous brain network transitions during the pre-stimulus period predict correct or incorrect somatosensory perception in an ambiguous tactile task.

The paper investigates how temporal information in subsecond sensory stimuli is encoded in primary visual cortex using a sensory discrimination task in mice.

The paper investigates how the brain executes programs by analyzing neural activity during mental program execution, finding that fast neural sequences represent candidate solutions.

This paper introduces a hybrid SEIR-FitzHugh-Nagumo model to simulate information flow in neural networks, showing improved directional information flow and reduced latency compared to conventional models.

This study examines how sevoflurane anesthesia affects neural activity in primate cortex, revealing non-uniform Up/Down state dynamics and differential sensitivity between prefrontal and parietal regions.