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The Program in Neural Computation (PNC) is a graduate training program in computational neuroscience for students seeking training in the application of quantitative approaches to the study of the brain. Specifically the program is designed to take advantage of the world class strengths of Carnegie Mellon University and our partner institution, the University of Pittsburgh, in areas including computer science, machine learning, statistics and dynamical systems and to train students to apply these tools to critical problems in neuroscience.
The PNC is a new Ph.D. program, but it is based on the highly successful, but non degree granting training program of the Center for the Neural Basis of Cognition (CNBC). Both these programs require training in neuroscience ranging from the cellular toi cognitive levels. The PNC also requires extensive training and a research focus in at least one quantitative area. Thus, the PNC program will be especially attractive to students coming from majors in math, computer science, statistics, engineering or physics who want to apply approaches from these disciplines to the understanding of brain function.
Neuroscientists are applying new technologies to acquire and analyze
large data sets, and more and more are using quantitative models to
understand the great complexities of neurobiological systems. As a
result, quantitative methods have become centrally important in the
field of neuroscience. At the same time, the number of investigators
with requisite skills who are actively engaged in this domain of
research is relatively small. There is a widely recognized need for
increased training in the application of computational, mathematical,
and statistical methods to biology and medicine, and to problems in
neuroscience in particular. These points have been emphasized in recent
articles about the field of neuroscience and in recent NIH calls for
grant applications.
Neuroscience has historically been heavily influenced by
quantitative approaches. There have been important advances through
the use of quantitative methods in neurophysiology, and there has been
a continuing stream of related work within mathematics and applied
physics. More recently, engineers, computer scientists, and
statisticians have contributed to the field, expanding further the
definition of computational neuroscience. We believe that the
environment at Carnegie Mellon and the University of Pittsburgh have
much to offer to students interested in these approaches and thus we
have founded a PhD Program in Neural Computation here at Carnegie
Mellon, in collaboration with colleagues at the University of
Pittsburgh. This
program is designed to attract students with strong quantitative
backgrounds and to train them in quantitative disciplines relevant to
neuroscience and also to provide them the essential background in
experimental neuroscience. In doing so we would bring to bear the
special strengths of our institution and the unique neuroscience
community here in Pittsburgh. Training faculty and courses will be
drawn both from CMU and Pitt as described. The PNC PhD program is
designed to capture students with backgrounds in computer science,
physics, statistics, mathematics, and engineering who are interested in
computational neuroscience, particularly with an emphasis on
quantitative methods from computer science, machine learning,
statistics and nonlinear dynamics.
The Program in Neural Computation (PNC) is a new program, but it is administered by the Center for the Neural Basis of Cognition (CNBC),
which was established in 1994 to foster interdisciplinary research on
the neural mechanisms of cognitive function. The CNBC now comprises 81
faculty having appointments in at least 17 departments (CMU: computer
science, machine learning, robotics, statistics, biomedical
engineering, biological sciences, psychology; PITT: neurobiology,
neuroscience, neurology, psychiatry, communication science, psychology,
radiology, bioengineering, history and philosophy of science,
mathematics).
The program consists of the following core activities:
- Coursework in computational neuroscience, quantitative methodologies and experimental neuroscience
- Exposure to experimental approaches through rotations or thesis research
- Training in teaching, scientific presentations and responsible conduct of research
- Successful defense of a Ph.D. Thesis
Coursework and program milestones are described elsewhere.
Advising and Student Evaluation
Students are assigned early in their first year an
advisor to guide the student in selecting courses and beginning his or
her initial research project. By the end of the summer following the
first year students must identify a thesis advisor. This faculty
member also will serve as the student’s academic advisor.
The Program in Neural Computation is supervised by a faculty
steering committee appointed by the CNBC education committee. The
Co-chairs of the CNBC education committee will serve as directors of
the graduate program. Graduate students meet with this committee for
approval of their curriculum, particularly for elective selections.
This committee also approves thesis committees and project committees.
Twice each year, the faculty steering committee reviews the progress of
each student in all aspects of the program. The results of this
evaluation will be communicated to the student by the Co-directors of
the CNBC.
Other program activities:
PNC students participate with CNBC certificate students in the following co-curricular activities.
The CNBC colloquium series is a student-run speaker series that
brings eminent scientists to Pittsburgh. Students have played a major
role in the selection and hosting of speakers throughout the years;
faculty provide input on speaker selection, but the students do all the
voting and interact extensively with the speakers during their visits.
The Brain Bag research seminars meet approximately bi-weekly
throughout the academic year on Monday evenings. At each Brain Bag, a
student gives a brief talk describing research in progress.
The CNBC retreat has been an important part of the process of
creating an integrated inlellectual community. The retreat provides a
venue for informal discussions of important topics of general interest
to members of the community, and introduces the students and faculty to
recently added CNBC faculty members through a series of 1/2 hour talks,
held on a Saturday and the following Sunday morning. Another important
element of the retreat is a set of interdisciplinary evening
discussions. Participants break up into small groups cutting across
levels of analysis and research methods to discuss topics of
interdisciplinary interest.
CNBC Friday Seminars are an occasional seminar series at which in-house and outside speakers present in an informal and interactive setting.
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