Our laboratory aims to understand the mechanisms through which dynamic engagement between neural networks is achieved. Although there has been great effort dedicated to mapping the anatomical connectivity between brain regions, we still have a poor understanding of how neural networks selectively communicate to one another in service of a computational need. This selective engagement is critically necessary for filtering information from multitudinous afferents and coordinating processing between regions that must communicate for successful computation. The ability to selectively coordinate information processing across regions enables an organism to attend to information relevant to behavioral circumstances. Neural oscillations can reflect the recruitment of cells into functional circuits and the successful coordination of neural network activity. Studying network oscillations during behavior can thus provide insight into the flexible participation of cells in local and cross-regional circuit processes.
Our projects combine computational and statistical models with in vivo electrophysiology to 1) identify elements within neural networks that give rise to rhythmically identifiable processing states and 2) test the impact of rhythmic coordination upon successful network engagement in rodent and human models. In addition, we record neural signals across scales in order to characterize the cellular and systems level processes that give rise to the electroencephalogram (EEG) signal and develop tools for translating the EEG signal into underlying local network dynamics.