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CNBC

Center for the Neural Basis of Cognition

The CNBC is a joint venture of the University of Pittsburgh and Carnegie Mellon University. Our center leverages the strengths of the University of Pittsburgh in basic and clinical neuroscience and those of Carnegie Mellon in cognitive and computational neuroscience to support a coordinated cross-university research and educational program of international stature. In addition to our Ph.D. program in Neural Computation, we sponsor a graduate certificate program in cooperation with a wide variety of affiliated Ph.D. programs.

Within the CNBC, our over 200 world-class faculty and trainees are investigating the cognitive and neural mechanisms that give rise to biological intelligence and behavior. Research topics include affective, cognitive, linguistic, perceptual, motor and social systems in both normal and disordered populations, as well as computational neuroscience. The CNBC also promotes the translation of findings from basic research into applications for medicine, education, robotics and artificial intelligence.

 
CNBC Researchers Extract the Brain’s Internal Model

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CNBC Faculty members Steve Chase and Byron Yu (CMU Biomedical engineering) and former CNBC student Matt Golub (now a postdoc in CMU Electrical and Computer Engineering) have developed a method to extract an internal model the brain uses to facilitate motor control. To produce accurate movements in light of delayed sensory feedback, the brain makes predictions on the basis of a model of how the world works to aid on-line computations. Using a brain computer interface (BCI), the researchers were able to observe the development of a subject’s internal model during motor behavior. Strikingly, when learning of the use of the BCI, the subject’s internal model is often wrong, failing to match the world. The work is detailed in eLife and more information is available here.

 
CNBC Professor Tai Sing Lee Receives $12M Grant

cell_1.jpg The CNBC is pleased to announce that Tai Sing Lee, a professor in the Computer Science Department and the Center for the Neural Basis of Cognition (CNBC), is funded by the Intelligence Advanced Research Projects Activity (IARPA) through its Machine Intelligence From Cortical Networks (MICrONS) research program.  Lee will work with co-principal investigators Sandra Kuhlman, assistant professor of biological sciences at Carnegie Mellon and the CNBC, and Alan Yuille, the Bloomberg Distinguished Professor of Cognitive Science and Computer Science at Johns Hopkins University, to discover the principles and rules the brain's visual system uses to process information. This deeper understanding could serve as a springboard to revolutionize machine learning algorithms and computer vision. You can read more here.

 
John Anderson awarded a 2016 Atkinson Prize in Psychological and Cognitive Sciences from the National Academy of Sciences

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The CNBC is pleased to announce that John Anderson (CMU Psychology and CNBC) has been awarded a 2016 Atkinson Prize in Psychological and Cognitive Sciences from the National Academy of Sciences (USA). The prize includes a gold-plated bronze medal and $100,000 and recognizes Anderson for his “foundational contributions to systematic theory and optimality analysis in cognitive and psychological science and for developing effective, theory-based cognitive tutors for education.” Mike Tarr, former CNBC co-director (CMU) and current head of Psychology (CMU), notes: “The fields of cognitive sciences and psychology have been fundamentally changed by John Anderson’s incredible body of theoretical work. The impact of his research on cognitive tutors is now creating similar change in student learning. Scientists and society are indebted to John for his contributions, and I am gratified that the academy is honoring him with this award.” More information about the award can be found here.

 
Carnegie Mellon Develops New Method for Analyzing Synaptic Density

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High-throughput, Machine-Learning Tool Could Help Researchers Better Understand Synaptic Activity in Learning and Disease

Alison Barth, professor of biological sciences and interim director of Carnegie Mellon’s BrainHub neuroscience initiative along with researchers Saket Navlakha, formerly of Carnegie Mellon and now at the Salk Institute for Biological Studies; Nicholas J. Audette, Dylan D. McCreary, and Joe Suhan of Carnegie Mellon’s Department of Biological Sciences and the CNBC; and Ziv Bar-Joseph of Carnegie Mellon’s Machine Learning Department and Lane Center for Computational Cancer Research have developed a new approach to broadly survey learning-related changes in synapse properties.  In a study published in the Journal of Neuroscience and featured on the journal’s cover, the researchers used machine-learning algorithms to analyze thousands of images from the cerebral cortex. This allowed them to identify synapses from an entire cortical region, revealing unanticipated information about how synaptic properties change during development and learning.  The study is one of the largest electron microscopy studies ever carried out, evaluating more subjects and more images than prior researchers have attempted. Read more.

 
Elucidating the Motor Code

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Scientists at Pitt Neurobiology and Systems Neuroscience Institute just broke the code used by the brain's primary motor cortex to control movements. A long standing debate has existed concerning the output signals from the brain to the spinal cord. Some have argued that the brain encodes force. Others have suggested that the brain encodes movement direction. In a paper published in Science, Darcy Griffin, Donna Hoffman and Peter Strick (University of Pittsburgh Systems Neuroscience Institute and CNBC) investigated the corticomotoneuronal (CM) contribution to movement. CM cells are a subset of primary motor cortex neurons that make direct monosynaptic connections with spinal motor neurons. The authors found that this subset of neurons were "functionally tuned". In other words, individual CM cells encoded a single muscle function. When we move, individual muscles are used for many functions; as agonists to generate force, fixators to prevent unwanted joint movement and asantagonists to brake movement. This exciting finding shows that the brain uses a population of CM cells, which are connected to the same muscle, to control different functions of that muscle. Thus, the primary motor cortex encodes the functional use of a muscle. The paper is available here.