Our lab combines large scale data-mining and machine-learning techniques with hypothesis-driven experimental research to understand the relationships between the human frontal lobes, cognition, and disease. In particular, we are focused on the role that neural oscillations play in coordinating neural computation and communication.
Ultimately our research goal is to construct an understanding of cognition built on the first principles of neurophysiology. Rather than asking, “What brain regions correlate with working memory or attentional load?” we ask, “Given what we know about the computational properties of neurons and neural systems, how can neural systems interact to give rise to cognitive phenomena we equate with ‘attention’ and ‘working memory’, and what are the behavioral and cognitive limitations and consequences of these biological constraints?”
In collaboration with his wife, Jessica Bolger Voytek, Dr. Voytek built and published brainSCANr, an algorithmic approach to aggregating information from more than 2 million peer-reviewed neuroscience articles. This project was his first attempt at automated science: using machine-learning tools to algorithmically generate novel hypotheses. Our philosophy with regards to the role of data-driven approaches to neuroscience is that large scale data analytics can complement and guide in-lab experimental research, but should not replace it.