My laboratory studies rhythmic patterns of activity that arise in networks of interacting neurons. When neurons interact in a repeated and stereotyped manner, rhythmic temporal constraints are placed upon their ability to receive and send information, which dramatically and predictably influences their ability to transform information. My laboratory aims to uncover the rhythmic subcircuit interactions that give rise to specific information transform abilities. This direction of research entails a thorough characterization of cell-type specific engagement in local and cross-regional neural rhythms, and statistical modeling of their dynamic engagement across changing behavioral states. In this manner, we aim to identify the range of network interactions that support different behavioral demands.
Work conducted in my laboratory has several key strengths. First, we design naturalistic behavioral paradigms for our rat model organism, utilizing our knowledge of both neuroanatomy and theory to reliably engage our brain structures of interest. Second, we utilize information theoretic approaches for characterizing neural representations of experience in single neuron spiking activity, allowing the identification of cell types that encode specific dimensions of experience. Third, we thoroughly characterize neural oscillatory dynamics across different behavioral states, and relate the spiking activity of different cell types to their engagement in specific rhythmic subcircuits using machine learning techniques. From our combined approaches, we have the ability to uncover evolving landscapes of rhythmic neural circuit interactions, and tie cell engagement in distinct rhythmic circuits to specific information processing abilities. Our work critically addresses questions regarding when and how neurons interact to fulfill a computational need.