Research Areas - Computational Neuroscience Research in the Computer Science Department at Carnegie Mellon
Computational neuroscience research seeks to understand how the brain learns and computes to achieve intelligent behavior. Computational neuroscientists build artificial systems and mathematical models to explore the computational principles underlying perception, cognition, memory and motor behaviors; they also apply mathematical and machine learning techniques to decode neural data. It is an interdisciplinary endeavor at the intersection of computer science, neuroscience, cognitive psychology, physics, engineering, mathematics, and statistics.
In collaboration with the Center for the Neural Basis of Cognition (CNBC), CSD offers educational programs in computational neuroscience through an undergraduate inter-college minor in Neural Computation , as well as a CNBC specialization in the Ph.D. program of the Computer Science Department.
Pittsburgh is a vibrant environment for research in computational neuroscience, endowed with the nationally renowned CMU-Pitt joint Center for the Neural Basis of Cognition (CNBC) and the Center for Cognitive Brain Imaging (CCBI). These centers provide a wide range of research opportunities in neuroscience, psychology and computational neuroscience.
CSD faculty working in the area of computational neuroscience:
John 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.
Jessica Hodgins is a Professor in the Robotics Institute and Computer Science Department at Carnegie Mellon University and part-time Director of Disney Research, Pittsburgh. Her research focuses on computer graphics, animation, and robotics with an emphasis on generating and analyzing human motion.
Tai Sing Lee, Associate Professor of Computer Science and Neural Basis of Cognition. He is interested in the computational principles and neural basis of perception, 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.
Tom M. Mitchell, Fredkin Professor of Computer Science, and department head of the Machine Learning Department. 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.
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.
Nancy Pollard, Associate Professor in the Robotics Institute and the Computer Science Department at Carnegie Mellon University. Her primary research objective is to understand how to create natural motion for animated human characters and humanoid robots. She also studies dexterous manipulation in human subjects.
Dave 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.
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