Neural dynamics and brain function

The paper defines criticality in low-dimensional dynamical systems and shows it enhances information processing in neural models.

The paper investigates how working memory stores durations, finding that the number of durations, not their total length, affects recall precision and is reflected in alpha and beta oscillations.

The paper shows that an optimal inhibitory-to-excitatory synaptic strength ratio enables both slow (beta) and fast (gamma) neural oscillations, enhancing communication efficiency and information capacity.

This study investigates whether auditory deviants evoke cortical state changes under anaesthesia, finding that they trigger an initial prediction error followed by a prolonged cortical Up state.

This paper introduces relational homeostatic scaling, a synaptic scaling rule that enables stable rate-code transmission in neural circuits despite noise and heterogeneity.

The paper uses an RNN model to replicate motor inhibition dynamics in monkey premotor cortex, showing how internal network states influence behavior and movement suppression.

This study shows that structured firing sequences emerge in human and murine brain organoids and neonatal brain slices, suggesting innate rather than experience-dependent neural coding.

This study shows that high-amplitude oscillatory events coordinate large-scale cortical networks during decision-making and attention, revealing a new mechanism for dynamic brain function.

This study shows that a biologically grounded spiking neural network model of V1 learning exhibits sparse coding, balance, and decorrelation as emergent properties.

RealtimeDecoder is a Python-based system for real-time clusterless decoding of neural data with low latency and no spike sorting, demonstrated in rodent hippocampus recordings.

The paper shows that neural activity in non-task-coding dimensions can drive behavior through transient amplification, challenging the assumption that only task-relevant dimensions are behaviorally significant.

The paper shows that neural activity perturbations improve the accuracy of inferring across-region interactions in brain models.

This paper extends a data assimilation method to multivariate EEGs, enabling parallel estimation of sensor-level network structures and E/I balance changes.

TVB-Optim enables efficient parameter estimation for whole-brain network models using automatic differentiation, achieving faster and more scalable optimization than traditional methods.

The paper investigates MT electrical oscillations in the hippocampus and neocortex of adult rats, finding region-specific frequency peaks and amplitude differences.

The paper develops biophysical and phenomenological models to explain in vivo burst firing in electrosensory pyramidal cells, showing how intrinsic conductances, Ca2+ dynamics, and synaptic noise interact to produce bursting activity.

This study shows that chewing rhythm synchronizes brain theta oscillations during working memory, enhancing cognitive performance through neural entrainment.

This paper introduces a deep spiking neural network for detecting auditory spatial attention from single-trial EEG, achieving 90% accuracy with 10% data.

This study shows how feedback reshapes neural representations in vlPFC through oscillatory dynamics, making them closer to future retrieval states.

The paper identifies triple cross-frequency coupling modes in brainstem oscillator networks, showing cooperative interactions between delta, theta, and gamma oscillations that support homeostatic regulation.

The paper analyzes the relative phase of membrane potential theta oscillations between hippocampal neurons during a virtual spatial task, revealing spatially coded phase shifts independent of spiking activity.

The paper shows that mice use orthogonal neural subspaces for context-specific decision-making in foraging tasks, supporting flexible cognition through modular learning and strategy switching.

The paper shows that perceptual reliability reduces membrane potential variability in cortical neurons during task-relevant stimuli.

The paper introduces JO_SCPLOWAXLEYC_SCPLOW, a framework for differentiable simulation of biophysical neuron models, enabling efficient parameter optimization and task-driven training of large-scale models.

The paper investigates how structural brain connections enable diverse dynamic patterns by testing if neuronal activity near a critical regime explains structural-functional relationships.

This study shows that changes in aperiodic EEG activity, not just oscillations, reflect cognitive state changes during working memory tasks, suggesting a need to include aperiodic components in EEG analysis.

This study shows that type-I nNOS neurons regulate neural and vascular dynamics in the brain by altering activity patterns and blood volume responses.

This study shows that visual information is encoded more accurately at specific alpha phases during sustained perception, using EEG and phase-dependent decoding.

This study identifies two distinct beta bands in brain activity using MEG and a Hidden Markov Model, showing different roles in movement initiation, maintenance, and termination.

The paper defines neural code symbols as variable-duration packets in auditory spike trains, showing this approach enables real-time readout and maximizes information capacity.