This project examines whether the combination of electrical brain stimulation and cognitive training can improve cerebral efficiency and plasticity, while simultaneously testing a possible neurobiological mechanism for cognitive fatigue. Results will inform the design of interventions aimed at improving cognitive performance and learning capacity.
How does learning impact neural networks in the primary visual cortex? This project will investigate learning effects at the neural network level by combining two-photon calcium imaging in animals learning an orientation discrimination task with a state-space analysis approach. Our proposal aims to identify a fundamental learning mechanism that leverages the power of large networks. Our results will help to define the scale at which learning effects need to be studied in the cortex.
How does the brain control the processing of different kinds of information that sometimes need to cooperate and sometimes need to compete, such as sensory information from the outside world versus abstract ideas and relationships? Bridging computational engineering methods, brain imaging techniques, and cognitive neuroscience, this study will test whether the strength of different interactions among multiple brain areas is related to how well an individual person is able to learn different kinds of information. Results will inform our understanding of the mechanisms behind how different parts of the brain communicate with each other. The results also may have implications for treating ADHD, autism, and other disorders with altered interactions between brain areas, and for designing educational methods tailored to the learning strengths and weaknesses of a broad range of typically developing individuals.