Neural Mechanisms of Perception and Abstraction

Cognitive function requires abstracting meaning from our environment.  Internal models of the external world can be thought of as a set of memorized predictions.  These predictions are used to carry out behavior and are learned through actions and outcomes.  Outcomes that match predictions serve to reinforce existing predictions while unexpected outcomes drive predictions to be updated.   Our lab studies how the brain generates knowledge from these experiences,  We take an integrative approach by combining large-scale in vivo imaging technology with molecular and genetic tools in the awake-behaving animal, focusing on the following topics:

Circuits and Computations Underlying Abstract Knowledge.  
​We are studying how cortical circuits can extract and classify sensory stimuli and formulate abstract representations through reinforcement and error-driven learning.

Intrinsic Determinants of Cognitive Variability.
​We are investigating how variations in genetic and circuit properties can shape our cognitive abilities and personalities to make us individuals.  

Multi-Modal Tools for Dissecting Circuits Across Scales. 
​We are developing a suite of optical technologies for measuring activity of neuronal populations across a range of spatial and temporal scales. These tools are integrated with dense, multi-modal anatomical and molecular measurements for elucidating circuit function.