Topic: A network neuroscience of human learning: Towards quantitative theories of brain and behavior
Meeting dates: February 22-24, 2018
Danielle S. Bassett, Bioengineering (BE), Electrical and Systems Engineering (ESE), Univ. of Pennsylvania
Scott T. Grafton, Neuroscience, Institute of Collaborative Biotechnologies Univ. of California, Santa Barbara
Objectives: Efforts to describe learning empirically can be greatly expanded by quantitative theories that map changes in neurophysiology to changes in behavior. Recent advances in network science offer tools and a general perspective that may be particularly useful in understanding types of learning that are supported by distributed neural circuits. Recent applications of these tools to neuroimaging data provide important insights into adaptive neural processes, the attainment of knowledge, and the acquisition of new skills, forming a network neuroscience of human learning. While promising, the tools have yet to be linked to well-formulated models of behavior used in cognitive psychology. This working group will develop the mathematical methodological necessary to connect network approaches to neuroscience data with quantitative models of behavior. This intersection is critical for fundamental, quantitative theories of brain and behavior across spatial scales and species. The group will seek to develop tools and models for the networks involved in learning, which are inherently multi-layered and embedded, including spatially distributed circuits in cortex and local networks in the basal ganglia and cerebellum. The group will target specific computational and theoretic challenges for mathematical development including models for non-stationary network dynamics, coupled multilayer stochastic block models and dynamics atop them, and extensions of temporal non- negative matrix factorization to annotated graphs. These efforts will lead to new mathematical techniques that will benefit the mathematics community. To evaluate techniques, the working group will develop challenge problems using extensive datasets available from the participating neuroscientists.
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