Karpova Lab

Behaving optimally in the face of whatever uncertainty the environment poses is often a computationally challenging problem. Animals may employ different behavioral strategies depending on how much they know about their environment (or the task) and on whether they are trying to maximize the immediate or the long-term reward. We have implemented a task, in which we can assess the neuro economic computations that underlie behavioral choices in a stochastic and changing environment. Our initial studies have shown that rodents are remarkably efficient at learning this complex task as well as at adapting their behavior in the face of unpredictable environmental dynamics within the task. We are beginning to address the precise computations that they use through a combination of computational modeling and recordings of population activity in the prefrontal cortex. In the future, we plan to explore the precise role of different regions in the prefrontal cortex and their modulation by mono amines in the behavioral flexibility that our animals display in this complex task.

In the real world, animals are often confronted not only with a nonstationary environment but also with a competitive setting where other active agents compete with them for the available resources. In such situations, the optimal behavioral strategy involves not only the above-mentioned computations but also the ability to adapt to the opponents’ strategy. A key capability under such circumstances is to have maximally unpredictable behavioral sequences to prevent other agents from learning and thereby exploiting one's own behavioral strategy. We have shown that rodents are remarkably efficient at finding an optimal behavioral strategy in a mixed-strategy game. Furthermore, using the optogenetic approach we have found initial evidence for the importance of specific regions in the prefrontal cortex for implementing this strategy. We are now planning to explore precise neural mechanisms animals employ in implementing this strategy using recordings of population activity in the identified regions.

The above mentioned experiments do not replicate the richness of social interaction. We are, therefore, planning to investigate social interaction in pairs of rodents, requiring two animals to cooperate or compete for a reward. We aim to correlate and manipulate the activity in the various regions of the prefrontal cortex to determine their role in the observation and optimization of social interaction in this game-theoretic task.

We have a strong interest in addressing how the neural circuitry underlying adaptive decision making is perturbed in neuropsychiatric disorders. In collaboration with the lab of Josh Dudman we are beginning to explore the network and behavioral deficits in the MitoPark mouse model of Parkinson’s disease. Our hope is to not only reach a deeper understanding of the perturbations that accompany progressive loss of dopamine neuromodulation but to establish quantitative assays that can be used to screen potential pharmacological and genetic therapeutic approaches.

Understanding circuit computations will be impossible without investigating specific functions of different cell types. The ability to target specific cell types has been the central focus of many large-scale genome targeting projects in mice, making it an attractive genetic model for circuit research. This capability has not yet been extended to rats, which are often the preferred behavioral model when complex tasks need to be implemented. Furthermore, even in mice, the number of different cell types that have been successfully targeted by genetic means has been rather limited. To circumvent these two limitations, in collaboration with Loren Looger, Josh Dudman, and Dave Schaffer of UC Berkeley we are pursuing an alternative approach by developing viral reagents for selective targeting of specific cell types through directed evolution of adeno-associated virus tropism.