Hannah M. Stealey

The paper investigates single-neuron dynamics during neurofeedback adaptation in rhesus macaques, finding that inhibitory interneurons are more active and engaged in learning compared to excitatory pyramidal neurons.

This study tracks individual neurons over time to show that while spike waveforms remain stable, some functional properties like firing rates and tuning can change, indicating a balance between neural stability and plasticity.

This study examines how individual neurons adapt during behavioral training and finds that neurons with strong population co-variation and prior behavioral contribution are less likely to change their activity.

This paper demonstrates neurofeedback using VTA LFP signals in non-human primates, showing transferable control strategies and potential for clinical applications.

This paper introduces a deep learning model to analyze multiscale brain activity dynamics and demonstrates its effectiveness in capturing causal interactions across scales.

The paper investigates how neural spiking variability affects adaptation during a brain-computer interface task in rhesus macaques, finding that population variance metrics differ between easy and hard adaptation conditions.