Training in Computational Neuroscience: From Biology to Model and Back Again
Carnegie Mellon University
Computational Neuroscience at Carnegie Mellon and the University of Pittsburgh is understood broadly, but training emphasizes multidisciplinary education, knowledge of experimental methodology, and collaborative research. Funding is used to provide fellowships to graduate students and undergraduate students in the two universities, and to support a summer program for quantitatively-oriented undergraduates across the nation, so that they can explore this exciting field. Faculty come from many disciplines across both universities and have research interests ranging from computational and dynamical system modeling of neural circuits and systems, to development and application of statistical and machine learning techniques for neural data analysis. Student projects span all levels of neuroscience, from molecular biology to electrophysiology to neuroimaging and behavior. The training program is administered by the Center for the Neural Basis of Cognition, an umbrella organization that coordinates and facilitates activities among more than 100 faculty and 140 graduate students at the two universities.
Project Website: http://www.cnbc.cmu.edu/
Project Director: Robert E. Kass, Ph.D. firstname.lastname@example.org
This training program has both an undergraduate and a graduate training component, and focuses on interdisciplinary research at the boundary between quantitative sciences and neuroscience. Trainees (both undergrad and grad) will take a set of core courses that include closely intertwined computational and laboratory neuroscience courses. Research opportunities range broadly within neuroscience, with a particularly strong focus in three areas: (1) development of advanced technology for the neurosciences, including imaging and molecular biological approaches; (2) development of advanced data analysis methods for complex neuroscientific data; (3) theoretical approaches and computational modeling of the dynamics of neural circuits and their relationship to mental function.
Project Director: David W. Tank, Ph.D. email@example.com
The University of Chicago
The goal of this program is to introduce undergraduates from a variety of disciplines to quantitative training in neuroscience, with the intention that students will continue on to graduate training in this area. The program has two components. The first component is a summer program in which undergraduate students from both the University of Chicago and other colleges and universities participate in research in faculty laboratory. Students also attend three seminars each week that introduce them to a wide range of neuroscience topics. The second component supports University of Chicago students who are doing extensive research projects with faculty during their junior and senior years. These students also take structured courses in computational neuroscience.
Project Director: Philip Ulinski, Ph.D. firstname.lastname@example.org
University of Pennsylvania
The focus of the Integrated Interdisciplinary Training in Computational Neuroscience program is to integrate neuroscience and quantitative studies through course work and extensive research training. The predoctoral program trains students in neurophysiology, data analysis, and modeling with the objective of producing scientists capable of investigating mechanisms of computation in neural circuits. The undergraduate program draws a select group of exceptional students from both the biological and physical science domains for coursework and integrated experimental/modeling research projects to enable them to succeed in graduate studies in computational neuroscience. The third component is a summer research program for undergraduates from Penn and other institutions. A distinguishing focus of this program is the application of computational neuroscience to neurological and psychiatric disorders.
Program Director: Michael J. Kahana, Ph.D. email@example.com
Search for Funding
Discusses the importance of quality mentorship in drug abuse research and offers suggestions for creating a successful mentor and mentee relationship.