Uri T. Eden

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.

This study shows rats can learn to generate specific remote hippocampal representations through neurofeedback without external cues or behavioral reports.

The paper shows that epileptic spikes during sleep block sleep spindle production in thalamic recordings of patients with epileptic encephalopathy.

This paper introduces a photothrombotic stroke model in mice that reproduces human epilepsy electrographic biomarkers, showing increased spike ripples and delta power in the stroke hemisphere.

The paper identifies individualized temporal patterns as the main determinant of sleep spindle timing in humans, using data from 1008 participants.

The paper evaluates phase estimation methods for neural rhythms, showing agreement only when uncertainty is low and proposing improved practices for phase estimation.

The paper creates a normative sleep spindle database from 772 healthy children and shows age-specific patterns in spindle features across development.

The paper introduces modified spline basis functions for neural spiking data regression to improve statistical power and numerical stability in point process GLMs.

This paper introduces a state space phase estimator for real-time neural phase tracking, showing improved performance over existing methods in simulations and in vivo data.

This study develops a state space model to analyze hippocampal replay events in rats, finding that most replay events contain spatially coherent content and often occur at real-world speeds.

This study investigates hippocampal replay during memory-guided behavior and finds that replay content reflects past experiences rather than future choices.

The paper addresses convergence issues in GLMs for spike train data by evaluating multiple methods on rat somatosensory cortex data.

The paper proposes a computationally efficient approximate point process filter using mixture models for real-time decoding of neural spiking activity in multidimensional spaces.

The paper introduces time and rate rescaling transformations for assessing goodness-of-fit in marked-point process models of neural population coding using uniformity tests.

The paper introduces a new framework for analyzing neural synchrony across brain regions using state-space coherence, applied to study task-related neural activity in a Stroop task.

The paper introduces a direct decoder model for Bayesian state-space modeling to decode neural activity without an encoding step, achieving comparable performance to existing methods.

This study uses state-space modeling and intracranial stimulation to modulate hidden emotional states during conflict resolution, showing bidirectional effects on behavior.

This paper introduces a statistical framework to assess cross-frequency coupling while accounting for confounding effects, showing improved performance in simulations and applications to in vivo data.

The paper introduces a Bayesian encoding model for real-time adaptation of neural activity decoding in rats during maze exploration.

The paper develops a goodness-of-fit framework for neural population models using marked point process time-rescaling, applicable to both spike-sorted and clusterless models.