Mac Shine

Investigates how neural systems organize across multiple scales, from local circuits to global network dynamics. This project integrates computational models with empirical neurobiological data to understand principles of brain organization.

Examines the mechanisms of selective attention through the lens of thalamocortical circuitry. Explores how the thalamus gates information flow to cortex and mediates competitive selection between behavioral alternatives.

Studies how gain modulation in ascending arousal systems affects neural computation and behavior. Combines theoretical models of gain control with experimental measurements to understand how arousal states modulate sensory and motor processing.

Investigates the neural mechanisms by which cognitive control is gradually transferred from attention-demanding systems to automatic/subcortical systems during skill learning. Tracks circuit reorganization as behaviors transition from conscious to unconscious processing.

Develops methods to characterize how brain networks dynamically reorganize over behavioral timescales. Combines high-resolution neuroimaging with computational analysis to identify principles governing temporal network reconfigurations.

Compares neural dynamics and circuit organization across species to identify conserved and divergent principles of brain function. Uses comparative analysis to understand fundamental constraints on neural computation.

Explores how degenerative processes alter neural circuits and brain dynamics in age-related and neurodegenerative conditions. Uses imaging and computational modeling to identify early signatures of neurodegeneration and test intervention strategies.