Research Interests:

The lab is interested in smells, sounds, and how we learn about what they mean. This is an important problem because learning about sensory information grants us the ability to predict “what comes next” and how to get there. For example, the smell of fresh-baked cookies tells us food is available. Road signs tell us where to turn to acquire food, as well as which specific food is offered. We also discriminate cues which share the same meaning. The integrated representation of these features of the behavioral landscape - sensory information, rewards, the identity of those rewards, and the actions required to obtain them - has been called a “cognitive map”, the construction and use of which confer the ability to make predictions based on direct experience and to make inferences in novel situations. We study how and why cognitive maps form during learning, how cognitive maps are used to guide behavior, and how they evolve with experience. How do we apply knowledge about the structure of the world to learn, predict, and adapt?

To address these questions, our primary approach is to record populations, or ensembles, of cortical neuron spiking activity while rats perform complex behavioral tasks. We combine neurophysiological approaches with theory-derived behavioral tasks, genetic interference and imaging tools, and computational analyses. Our goal is to acquire a better fundamental understanding of integrated neural representations, their importance for learning, and, by extension, a better understanding of human cognition.