Big Question: How whole brain neural network connectivity predicts learning potential

Principal Investigator: Sarah London, Psychology; Marc Berman, Psychology

Funding Type: Seed

Focus Area: Complexity

Big Idea: The same experience is not always equivalently learned within and across individuals, suggesting that neural circuits shift between states that permit or limit the possibility of learning. We investigate the properties of neural circuits that regulate their receptivity to experience. Using an animal model that naturally undergoes fluctuations in the ability to learn from an experience, the zebra finch songbird, we examine patterns of whole-brain neural synchrony to define network properties associated with cognitive potential. In our proposed research we combine whole-brain connectivity network metrics with measures of learned behavior. With this combinatorial strategy, we can directly relate changes in brain network function with transitions through learning potential states within an individual. Results further our long-term goal to understand the accuracy of patterns of neural networks to predict an individual’s potential to learn from the current environment.