WORKSHOP: Quantitative Theories of Learning, Memory and Prediction

May (7) 8-9, 2014
Westin Arlington Gateway


As brain imaging and manipulation technologies become increasingly sophisticated, data sets that could potentially provide new insights into human cognitive function become more plentiful. However, a critical gap lies in the development of quantitative theories of higher order cognitive functions that capitalize on these data.  In this workshop, we focus on learning, memory, and prediction, which require complex, dynamic processes across large-scale distributed neural circuits. Our aim is to bring together expertise in theoretical physics, computational modeling, data acquisition, and cognitive neuroscience to discuss the next frontiers in theoretical models of higher order cognitive processes. Our goal is to outline a handful of theoretical areas where — given appropriate levels of funding — groundbreaking advances are tractable in the 5-10 year time frame with the collaborative efforts of both theoreticians and experimentalists.

Workshop Organizers
Danielle Bassett, University of Pennsylvania
Nancy Kopell, Boston University
William Bialek, Princeton University
Funder and Funder Contacts 
National Science Foundation
Krastan B. BlagoevProgram Director, Physics of Living Systems (Physics Division)
Betty K. Tuller, Program Director, Division of Behavioral and Cognitive Sciences

 

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