Human perception, decision-making, and action are remarkably dynamic. We want to understand how, when, and why this flexibility arises in the human brain. We take inspiration from cognitive, computational, and systems neuroscience perspectives to clarify:
- SIGNAL (Cognitive Stability; Intrinsic brain states; Gain control; Neural rhythms; Attention; Thalamocortical Loops) &
- NOISE (Neural variability; Output flexibility; Imprecision & uncertainty; Stochastic computations; Excitation/inhibition) in brain & cognition.
We pursue the notion that the brain has evolved efficient mechanisms to manage its actions in uncertain environments and test contributions of deep brain structures such as the thalamus to such capacity. In our studies, we blend experimental psychology, multimodal neuroimaging, and computational modeling - and pioneer new analytical approaches to draw more precise inferences from human neuroimaging (e.g., EEG, MEG, and fMRI) time series. Moving beyond the level of observation, we use ultrasonic deep brain stimulation to interact with presumed switchboards of brain dynamics.
In tandem with our pursuits to chart and shape the human brain mechanisms underlying flexible behaviors, we implement neuroinformatics advances towards FAIR (Findable, Accessible, Interoperable, and Reusable) neuroscience data management.