William Stanley Anderson

The paper shows that specific amygdala neurons in humans fire more during encoding of items later remembered, predicting episodic memory success, particularly in the right amygdala.

The paper identifies memory selective neurons in the human hippocampus that correlate with pattern separation and show behavioral relevance in episodic memory.

This paper uses GPU-accelerated optimization to identify synaptic reorganization patterns that produce Parkinsonian beta oscillations in a basal ganglia network model.

The paper uses zap-and-freeze electron microscopy to study synaptic vesicle dynamics in mouse and human brain slices, revealing conserved endocytic mechanisms.

This study shows that disrupting selective persistent neural activity in the hippocampus and ventral temporal cortex with electrical stimulation impairs human working memory performance.

This study shows that persistent hippocampal activity during working memory maintenance predicts long-term memory formation in humans.

This study uses a computational model to show phase-dependent stimulation can modulate theta-gamma coupling in the hippocampus, with implications for treating memory disorders.

This study investigates how theta-gamma phase amplitude coupling in the hippocampus supports working memory by integrating cognitive control and improving memory fidelity.

The paper analyzes phase-amplitude coupling in human 2D neural cultures to show that network-level interactions, not single neurons, drive coupling, suggesting PAC is a fundamental property of neural networks.

This study investigates theta-gamma phase-amplitude coupling in the human hippocampus during a working memory task and finds it correlates with memory performance, particularly during retrieval.

This study investigates neural processes during conflict resolution in cognitive control tasks and finds task-specific neural signatures that do not generalize across tasks.

The paper introduces and validates neural fragility as an EEG biomarker for identifying the seizure onset zone, showing higher prediction accuracy than clinician judgment.

The paper develops a network analysis algorithm to localize the epileptogenic zone using invasive EEG recordings, showing better agreement with clinical outcomes in successful surgeries.