I am interested in rewards, emotions, and how they influence decision making. Rewards signal the location of valuable energy sources, help individuals learn, guide appropriate action selection, and provide satisfaction. My lab focuses on neural circuits that mediate reward processing, affective responses, and volitional behaviors – including learning, deliberating, and decision making.
We use a combination of single-unit recording, optogenetics, computational modeling, and psychophysics to investigate neuronal and neural circuit coding of behaviorally defined reward-related variables. These behaviorally defined variables including value, risk, time, and motivation.
I am particularly interested in dopamine signaling – reward prediction error signaling – and how it motivates behaviors. My fascination with these neurons is due largely to their clear neurocomputational properties and consequences. I often wonder whether the neurocomputational clarity that we have achieved with dopamine neurons is due to our ability to unambiguously identify them using classical electrophysiology. If we could clearly identify and record from other neuron types during behavior, would their activity be as well-described by computational algorithms as phasic dopamine activity is by the prediction error?
One method for cell type identification is achieved by selective expression of opsin molecules. This method is most readily achieved in specially-bred mice. In monkeys and other wild type animal models, we are limited by a dearth of small, cell type-specific promoters to guide cell type-specific expression. Therefore, in a second line of research my lab uses genetic engineering and single-cell transcriptomics and to identify cell type-specific gene promoters that enable the study of specific cell types and neural circuits in wild-type animals.
