This project combines gene expression profiling using a novel high-throughput technique (single-cell RNA sequencing), computational analysis, and current theories of the cellular organization of a brain structure important in learning and memory to understand the mechanisms underlying learning.
This project will combine behavioral measures with a novel PET imaging application to investigate neural mechanisms underlying the relearning of taste preferences and food reward in bariatric surgery.
This project integrates expertise in neuroscience and early childhood education to explain how teacher and child capacities interact to shape cognitive and behavioral development in low-income children.
This project bridges educational theory, augmented reality novel gamification techniques, and multimodal machine learning to develop and refine a new augmented reality learning tool for human anatomy education.
The project applies findings from the cognitive science of language production to education. In addition, it proposes a parallel investigation of language learning in typically-developing children learning their second language and brain-damaged adults re-learning the language they have lost. As such, the project lies at the intersection of the three fields of cognitive science, education, and medicine.
This research project will reveal neural mechanisms of synaptic plasticity that allow hippocampal networks to flexibly generate goal-directed sequences for navigation and memory. Theoretical predictions may reveal new insights into neurological disorders affecting learning and memory.
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.
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.
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.
Our project combines molecular and behavioral approaches to identify changes in the microRNA system that impact learning and memory. Potential implications of this work are the identification of microRNA pathways that promote successful cognitive aging may lead to therapeutic interventions to combat cognitive decline in aging, as well as other learning disorders.