URI: http://hdl.handle.net/2144/1237

Research and training programs in BU’s Department of Cognitive & Neural Systems address two broad questions: How does the brain control behavior? How can technology emulate biological intelligence? The department provides advanced training and research experience for MA and PhD students and qualified undergraduates interested in the neural and computational principles, mechanisms, and models that underlie human and animal behavior, as well as the application of neural network architectures to the solution of technological problems. The affiliated Center for Adaptive Systems is a close research ally, with faculty from biology, computer science, engineering, mathematics, and psychology.

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Department chair: Ennio Mingolla Campus address: 677 Beacon Street Phone: 617-353-9481 Fax: 617-353-7755 Website: cns-web.bu.edu

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  • A Wireless Brain-Machine Interface for Real-Time Speech Synthesis 

    Guenther, Frank H.; Brumberg, Jonathan S.; Wright, E. Joseph; Nieto-Castanon, Alfonso; Tourville, Jason A.; Panko, Mikhail; Law, Robert; Siebert, Steven A.; Bartels, Jess L.; Andreasen, Dinal S.; Ehirim, Princewill; Mao, Hui; Kennedy, Philip R. (Public Library of Science, 2009-12-9)
    BACKGROUND. Brain-machine interfaces (BMIs) involving electrodes implanted into the human cerebral cortex have recently been developed in an attempt to restore function to profoundly paralyzed individuals. Current BMIs for ...
  • Hippocampal Conceptual Representations and Their Reward Value 

    Okatan, Murat (Frontiers Research Foundation, 2010-02-03)
  • Mindboggle: Automated Brain Labeling with Multiple Atlases 

    Klein, Arno; Mensh, Brett; Ghosh, Satrajit; Tourville, Jason; Hirsch, Joy (BioMed Central, 2005-10-5)
    BACKGROUND: To make inferences about brain structures or activity across multiple individuals, one first needs to determine the structural correspondences across their image data. We have recently developed Mindboggle as ...
  • A Vector-Integration-to-Endpoint Model for Performance of Viapoint Movements 

    Bullock, D.; Bongers, R. M.; Lankhorst, M.; Beek, P. J. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1997-08)
    Viapoint (VP) movements are movements to a desired point that are constrained to pass through an intermediate point. Studies have shown that VP movements possess properties, such as smooth curvature around the VP, that are ...
  • Adaptive Resonance: An Emerging Neural Theory of Cognition 

    Carpenter, Gail (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2000-09)
    Adaptive resonance is a theory of cognitive information processing which has been realized as a family of neural network models. In recent years, these models have evolved to incorporate new capabilities in the cognitive, ...
  • Texture Segregation By Visual Cortex: Perceptual Grouping, Attention, and Learning 

    Bhatt, Rushi; Carpenter, Gail A.; Grossberg, Stephen (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2006-07-28)
    A neural model is proposed of how laminar interactions in the visual cortex may learn and recognize object texture and form boundaries. The model brings together five interacting processes: region-based texture classification, ...
  • From Parallel Sequence Representations to Calligraphic Control: A Conspiracy of Neural Circuits 

    Bullock, Daniel (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2004-05)
    Calligraphic writing presents a rich set of challenges to the human movement control system. These challenges include: initial learning, and recall from memory, of prescribed stroke sequences; critical timing of stroke ...
  • Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System 

    Carpenter, Gail; Grossberg, Stephen; Rosen, David (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1991-06)
    A Fuzzy ART model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory ...
  • Brightness Perception, Illusory Contours and Corticogeniculate Feedback 

    Gove, Alan; Grossberg, Stephen; Mingolla, Ennio (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1994-11)
    A neural network model is developed to explain how visual thalamocortical interactions give rise to boundary percepts such as illusory contours and surface percepts such as filled-in brightnesses. Top-down feedback ...
  • Fast Learning VIEWNET Architectures for Recognizing 3-D Objects from Multiple 2-D Views 

    Bradski, Gary; Grossberg, Stephen (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1995-08-01)
    The recognition of 3-D objects from sequences of their 2-D views is modeled by a family of self-organizing neural architectures, called VIEWNET, that use View Information Encoded With NETworks. VIEWNET incorporates a ...
  • Adaptive Resonance Theory 

    Grossberg, Stephen (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2000-10)
  • Resonant Neural Dynamics of Speech Perception 

    Grossberg, Stephen (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2002-09)
    What is the neural representation of a speech code as it evolves in time? How do listeners integrate temporally distributed phonemic information across hundreds of milliseconds, even backwards in time, into coherent ...
  • ARTMAP-IC and Medical Diagnosis: Instance Counting and Inconsistent Cases 

    Carpenter, Gail A.; Markuzon, Natalya (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1996-05)
    For complex database prediction problems such as medical diagnosis, the ARTMAP-IC neural network adds distributed prediction and category instance counting to the basic fuzzy ARTMAP system. For the ARTMAP match tracking ...
  • Adaptive Resonance Theory 

    Carpenter, Gail A.; Grossberg, Stephen (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2009-05)
  • Adaptive Resonance Theory 

    Carpenter, Gail; Grossberg, Stephen (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1998-09)
  • Cortical Synchronizing and Perceptual Framing 

    Grossberg, Stephen; Gruenwald, Alexander (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1994-05)
    How does the brain group together different parts of an object into a coherent visual object representation? Different parts of an object may be processed by the brain at different rates and may thus become desynchronized. ...
  • Neural Dynamics of Learning and Performance of Fixed Sequences: Latency Pattern Reorganizations and the N-STREAMS Model 

    Rhodes, Bradley; Bullock, Daniel (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 2002-06)
    Fixed sequences performed from memory play a key role in human cultural behavior, especially in music and in rapid communication through speaking, handwriting, and typing. Upon first performance, fixed sequences are often ...
  • A Self-Organizing System for Classifying Complex Images: Natural Textures and Synthetic Aperture Radar 

    Grossberg, Stephen; Williamson, James (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1996-01)
    A self-organizing architecture is developed for image region classification. The system consists of a preprocessor that utilizes multi-scale filtering, competition, cooperation, and diffusion to compute a vector of image ...
  • Pitch-based Streaming in Auditory Perception 

    Grossberg, Stephen (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1996-02)
    This chapter summarizes a neural model of how humans use pitch-based information to separate and attentively track multiple voices or instruments in distinct auditory streams, as in the cocktail party problem. The model ...
  • Distributed ART Networks for Learning, Recognition, and Prediction 

    Carpenter, Gail A. (Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems, 1996-02)
    Adaptive resonance theory (ART) models have been used for learning and prediction in a wide variety of applications. Winner-take-all coding allows these networks to maintain stable memories, but this type of code representation ...

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