GGrantIndex
← Search

Training Program in Neurobiology of Information Storage

$201,849T32FY2016MHNIH

Northwestern University At Chicago, Evanston IL

Investigators

Linked publications & trials

Abstract

DESCRIPTION (provided by applicant): The Neurobiology of Information Storage Training Program (NISTP) is based in the Northwestern University Interdepartmental Neuroscience program (NUIN), emerging from within a multidisciplinary group of highly interactive investigators who have carried out research at different levels, and have successfully engaged in collaborative research on cellular, molecular, structural, network and system determinants of information storage. After the first 2 cycles of NIMH-supported training grant activity it is clear that NISTP is part of the fabric of the neuroscience community at Northwestern. Since its inception in 2003, NISTP has evolved by continually adding elements that enrich the training experience. Training includes a formal didactic component in the form of a special course focusing on the latest research in information storage neurobiology, three special lecture series each with the purpose of bringing the most outstanding scientists to interact with the trainees, a mock study section which provides both professional training and is a springboard for submission of an NRSA proposal (a NISTP requirement), the annual retreat that fosters a wide-range of survival skills with maximum trainee - faculty interaction, two different journal clubs reviewing recent research in the neurobiology of information storage, and a 'Buddy Program' that encourages trainees to see the 'bench-to-bedside' application of their fundamental research. With these value added components, we believe that trainees emerge from the training program both poised to advance research in fundamental biological mechanisms of learning and memory and well-positioned to develop novel translational applications.

View original record on NIH RePORTER →
Training Program in Neurobiology of Information Storage · GrantIndex