Our lab studies the algorithmic and neuronal basis of reward foraging decisions in flies, mice, and humans. To do this, we design trial-based reward foraging tasks to study how animals integrate reward history into their decisions (Fig.1A). Using computational models from reinforcement learning theory we gain insights how optimal agents solve the same tasks and compare the decision rules used by the artificial agents to the ones used by the animals 1,2.The decision rules that animals use to maximize their reward harvesting efficiency serves as a guiding principle to search for their neural correlates. For this we use extracellular electrophysiological recordings to track the single neurons and optogenetics to manipulate specific cell types in behaving animals (Kvitsiani D, et al Nature 2013). We also use optogenetics to optically tag specific interneuron cell-types in extracellular recordings to understand circuit level computations and the role of inhibition in shaping cortical activity.
The Kvitsiani group currently has projects available for Master students, PhD students, and post docs. Please contact Group Leader Duda Kvitsiani directly, if interested.