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Computational neuroscience, the computatioal study of brain
functions, has a glorious tradition at Carnegie Mellon.
Allen Newell, Herb Simon and John Anderson's ground-breaking work in symbolic artificial
intelligence, James McClelland and Geoff Hinton's pioneering
work on neural networks and parallel distributed processing
are both milestones in computational understanding of
human intelligence and cognition.
Today, faculty in computer
science, robotics, computational and statistical learning,
statistics, and psychology are applying multidisciplinary and
interdisciplinary techniques
to study the computational principles and neural basis
of perception, language, cognition, behavior
and natural intelligence.
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SCS Faculty and Research in Computational Neuroscience
Faculty in the School of Computer Science are engaged in a wide range of
cross-disciplinary research activities in computational neuroscience
in four major research centers:
(1) Center for
the Neural Basis of Cognition (CNBC),
(2) Robotics Institute,
(3) Center for Cognitive Brain
Imaging (CCBI),
and (4)
Pittsburgh Supercomputing Center
(PSC).
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John R. Anderson:
University Professor of Psychology and Computer Science.
Member of the National Academy of Sciences.
His research is concerned with contribution to the development to the
ACT-R architecture
which is a
computational model of human intelligence. One line of research is
concerned with the learning of high-performance skills like air traffic
control. The other is concerned with tracking brain correlates of
architectural components with fMRI. Lab:
ACT-R Research group.
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Tai Sing Lee
Associate Professor of Computer Science and Neural Basis of Cognition.
He is interested in the computational principles and neural basis of learning
and adaptation, and the nature of hierarchical computation in the visual systems.
He is working on these problems using an integrated and interdisciplinary approach
based on computational modeling, statistical analysis, and electrophysiology.
Lab: Active Perception Lab.
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Michael Lewicki
Associate Professor of Computer Science and Neural Basis of Cognition.
He is interested the computational principles underlying the ability
of the brain to represent and process complex, real-world patterns, including
the question of how to code sensory information, how this code is processed
to represent more abstract properties of the stimulus,
what are the basic computations underlying the formation of
perceptual invariances such as the ability to recognize words independent of
speaker or objects independent of orientation? Research in his
lab works toward these goals by developing and applying principles
of information representation and processing.
Lab:
Laboratory for Computational Perception and Statistical learning
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Tom M. Mitchell
Fredkin Professor of Computer Science,
and Director of
CMU's
Center for Automated Learning and Discovery.
His general interests lie in
developing computational models of brain function, grounded in observed data
from humans (e.g., fMRI, ERP, behavioral data). Recently Mitchell's group
has developed statistical machine learning algorithms that can be trained to
distinguish different cognitive processes in humans, based on their observed
fMRI brain activity. For example, they have trained their system to
distinguish whether a subject is reading a sentence or viewing a picture,
and whether the subject is reading a word about tools, buildings,
or vegetables.
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David Plaut
Professor of Psychology, Computer Science and Neural Basis of Cognition.
He uses connectionist/neural-network modeling, complemented by behavioral
studies, to investigate normal and impaired cognitive processing in the domain
of reading and language. His specific interests include early language
acquisition and phonological development, word reading, cross-linguistic
differences in morphological processing, and patterns of semantic impairments
following brain damage.
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David Touretzky
Research Professor of Computer Science and Neural Basis of Cognition.
He builds computational models of spatial representations in the
rodent brain, such as "cognitive maps" in the hippocampus, and
attractor networks in the head direction system. He is also
interested in cognitive robotics: developing high level perceptual and
motor primitives for describing robot behaviors. This work currently
uses the Sony AIBO dog robot.
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Faculty and Research in Allied Departments:
Faculty in the Department of Statistics at Carnegie Mellon
who are interested in applying statistical techniques to solve
problems related to functional imaging and neuronal ensemble data analysis.
Computational Neuroscience Training Programs in SCS
In collaboration with the
Center for
the Neural Basis of Cognition (CNBC), the School
of Computer Science offers three training tracks in computational
neuroscience:
Center for
the Neural Basis of Cognition (CNBC) also offers the following new Ph.D. program
in computational neuroscience:
Interested applicants should check the CNBC option in the SCS
Ph.D. program application forms.
To be admitted to
the CNBC training program, students must be first accepted into one
of the SCS Ph.D. programs or other CNBC affliated programs at Carnegie
Mellon or the University of Pittsburgh. Applicants are encouraged to
submit
a separate application to CNBC
before January 1st.
Students may also apply to CNBC after admission to one of the
three SCS Ph.D. programs. Please check
CNBC application and admission process.
and
CNBC program description and benefits.
Currently, there is no computational neuroscience
track in the undergraduate computer science curriculum.
There is however a wide variety of
CNBC courses
offered at Carnegie
Mellon and the university of Pittsburgh
in neuroscience and computational neuroscience.
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