Department of Cognitive and Neural Systems

The following list reflects the 2006/2007 faculty.

Chairman Stephen Grossberg

Director of Graduate Studies 
Barbara Shinn-Cunningham

Faculty

Daniel H. Bullock Associate Professor of Cognitive and Neural Systems and Psychology, College of Arts and Sciences. BA, Reed College; PhD, Stanford University

Gail A. Carpenter Professor of Cognitive and Neural Systems and Mathematics, College of Arts and Sciences. BA, University of Colorado; MA, PhD, University of Wisconsin, Madison

Michael A. Cohen Associate Professor of Cognitive and Neural Systems and Computer Science, College of Arts and Sciences. SB, Massachusetts Institute of Technology; PhD, Harvard University

Stephen Grossberg Chairman, Department of Cognitive and Neural Systems; Director, Center for Adaptive Systems; Director, Center of Excellence for Learning in Education, Science, and Technology (CELEST); Wang Professor of Cognitive and Neural Systems, College of Arts and Sciences; Professor of Mathematics, Psychology, and Biomedical Engineering. BA, Dartmouth College; MS, Stanford University; PhD, Rockefeller University

Frank Guenther Associate Professor of Cognitive and Neural Systems, College of Arts and Sciences. BS, University of Missouri, Columbia; MSE, Princeton University; PhD, Boston University

Ennio Mingolla Professor of Cognitive and Neural Systems and Psychology, College of Arts and Sciences. BA, Harvard University; MEd, Boston University; PhD, University of Connecticut

Michele Rucci Assistant Professor of Cognitive and Neural Systems, College of Arts and Sciences. BA, MA, University of Florence, Firenze (Italy); PhD, Scuola Superiore, Pisa (Italy)

Eric Schwartz Professor of Cognitive and Neural Systems, College of Arts and Sciences. Electrical, Computer, and Systems Engineering, College of Engineering. Anatomy and Neurobiology, School of Medicine. AB, Columbia College; MS, PhD, Columbia University

Barbara Shinn-Cunningham Director of Graduate Studies, Department of Cognitive and Neural Systems; Associate Professor of Cognitive and Neural Systems, College of Arts and Sciences; Associate Professor of Biomedical Engineering, College of Engineering. ScB, Brown University; MS, PhD, Massachusetts Institute of Technology

Mukund Balasubramanian Research Associate, Department of Cognitive and Neural Systems, College of Arts and Sciences. BA, BS, University of Texas at Austin; PhD, Boston University

Virginia Best Research Associate, Department of Cognitive and Neural Systems, College of Arts and Sciences. BMS, PhD, University of Sydney (Australia)

Yongqiang Cao Research Assistant Professor, Department of Cognitive and Neural Systems, College of Arts and Sciences. BS, Peking University, Beijing (China); MS, University of Technology, Dalian (China); PhD, York University, Toronto (Canada)

Arash Fazl Research Associate, Department of Cognitive and Neural Systems, College of Arts and Sciences. MD, Tehran University of Medical Sciences (Iran); PhD, Boston University

Daniel Franklin CELEST Director of Curriculum Development, Department of Cognitive and Neural Systems, College of Arts and Sciences. BA, Williams College; MTS, Harvard University; MBA, Boston University

Satrajit Ghosh Research Fellow, Department of Cognitive and Neural Systems, College of Arts and Sciences. BS, National University of Singapore; PhD, Boston University

Anatoly Gorshechnikov Research Associate, Department of Cognitive and Neural Systems, College of Arts and Sciences. BS, Belmont University; MS, Middle Tennessee State University; PhD, Boston University

Norbert Kopco Research Associate, Department of Cognitive and Neural Systems, College of Arts and Sciences. MSc (Dipl Ing), Technicka Univerzita, Kosice (Slovakia); PhD, Boston University

Geoffrey Stewart Morrison Research Fellow, Department of Cognitive and Neural Systems, College of Arts and Sciences. BS, University of Dundee (Scotland); MTS, Vancouver School of Theology (Canada); MA, Simon Fraser University (Canada); PhD, University of Ottawa (Canada)

Alfonso Nieto-Castanon Research Associate, Department of Cognitive and Neural Systems, College of Arts and Sciences. BS, MS, University of Valladolid (Spain); PhD, Boston University

Simon Overduin Research Associate, Department of Cognitive and Neural Systems, College of Arts and Sciences. BSc, Wilfrid Laurier University (Canada); PhD, Massachusetts Institute of Technology

Jonathan Polimeni Research Fellow, Department of Cognitive and Neural Systems, College of Arts and Sciences. BS, Johns Hopkins University; PhD, Boston University

Kevin Reilly Research Associate, Department of Cognitive and Neural Systems, College of Arts and Sciences. BA, College of the Holy Cross; MS, Columbia University; PhD, University of Washington, Seattle

Fabrizio Santini Research Associate, Department of Cognitive and Neural Systems, College of Arts and Sciences. MS, University of Rome, La Sapienza, Rome (Italy); PhD, University of Florence (Italy)

Timothy Streeter Research Associate, Department of Cognitive and Neural Systems, College of Arts and Sciences. BS, MS, University of New Hampshire; MA, Boston University

Massimiliano Versace Assistant Director, CNS Technology Lab for Science and Technology Outreach, Department of Cognitive and Neural Systems, College of Arts and Sciences. BA/MA, University of Trieste (Italy); PhD, Boston University

Tony Vladusich Research Associate, Department of Cognitive and Neural Systems, College of Arts and Sciences. BS, University of Queensland (Australia); PhD, Australian National University

Affiliated Faculty

Jelle Atema Director, Boston University Marine Program (BUMP). Professor of Biology, College of Arts and Sciences. Candidate, Doctorandus, Rijksuniversiteit te Utrecht (Netherlands); PhD, University of Michigan

Helen Barbas Professor of Health Sciences, Sargent College. BA, Kean College; MS, Kansas State University; PhD, McGill University (Canada)

H. Steven Colburn Director, Hearing Research Center. Professor of Biomedical Engineering, College of Engineering. SB, SM, PhD, Massachusetts Institute of Technology

Howard Eichenbaum Director, Center for Memory and Brain. Chairman, Department of Psychology; Professor of Psychology, College of Arts and Sciences. BS, PhD, University of Michigan

William D. Eldred III Professor of Biology, College of Arts and Sciences. BS, PhD, University of Colorado

Jean Berko Gleason Professor Emerita of Psychology, College of Arts and Sciences. AB, Radcliffe College; AM, PhD, Harvard University

Sucharita Gopal Professor of Geography and Environment, College of Arts and Sciences. BA, MSc, BEd, MPhil, Madras University (India); PhD, University of California, Santa Barbara

Catherine Harris Associate Professor of Psychology, College of Arts and Sciences. BA, Harvard University; PhD, University of California, San Diego

Michael E. Hasselmo Professor of Psychology, College of Arts and Sciences. AB, Harvard University; PhD, Oxford University (England)

Allyn Hubbard Professor of Electrical and Computer Engineering, College of Engineering. BS, MS, PhD, University of Wisconsin

Thomas Kincaid Professor of Electrical, Computer, and Systems Engineering, College of Engineering. BS, Queen’s University (Canada); SM, PhD, Massachusetts Institute of Technology

Mark Kon Professor of Mathematics, College of Arts and Sciences. BA, Cornell University; PhD, Massachusetts Institute of Technology

Nancy Kopell Professor of Mathematics, College of Arts and Sciences. BA, Cornell University; MA, PhD, University of California, Berkeley

Jacqueline A. Liederman Professor of Psychology, College of Arts and Sciences. BA, City College of New York; PhD, University of Rochester

Joseph Perkell Adjunct Professor of Cognitive and Neural Systems, College of Arts and Sciences. Senior Research Scientist, Research Lab of Electronics and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology. SB, Massachusetts Institute of Technology; DMD, Harvard School of Dental Medicine; PhD, Massachusetts Institute of Technology

Marc Pomplun Adjunct Assistant Professor of Cognitive and Neural Systems; Director, Visual Attention Laboratory; Assistant Professor of Computer Science, University of Massachusetts Boston. Vordiplom, Diplom, PhD, University of Bielefeld (Germany)

Adam Reeves Adjunct Professor of Cognitive and Neural Systems, College of Arts and Sciences; Professor of Psychology, Northeastern University. BA, PhD, City University of New York

Elliot L. Saltzman Associate Professor of Physical Therapy, Sargent College. AB, Harvard University; PhD, University of Minnesota

Robert Savoy Adjunct Associate Professor of Cognitive and Neural Systems, College of Arts and Sciences; President, HyperVision Incorporated; Assistant in Experimental Psychology, Director of fMRI Education, Instructor, Department of Radiology, Harvard Medical School. BS, MS, Massachusetts Institute of Technology; PhD, Harvard University

Robert Sekuler Adjunct Professor of Cognitive and Neural Systems, College of Arts and Sciences; Professor of Cognitive Neuroscience, Frances and Louis H. Salvage Professor of Psychology, Brandeis University. AB, Brandeis University; MS, PhD, Brown University

David Somers Associate Professor of Psychology, College of Arts and Sciences. BS, Harvey Mudd College; PhD, Boston University

Chantal Stern Director, Brain, Behavior, and Cognition Program. Associate Professor of Psychology and Program in Neuroscience, College of Arts and Sciences. BA, McGill University (Canada); DPhil, University of Oxford (England)

Malvin C. Teich Professor of Electrical and Computer Engineering and Biomedical Engineering, College of Engineering. SB, Massachusetts Institute of Technology; MS, Stanford University; PhD, Cornell University

Lucia Vaina Professor of Biomedical Engineering, College of Engineering; Research Professor of Neurology, School of Medicine. MS, University of Timisoara (Romania) and Urbino (Italy); PhD, Université Paris I (France); Dres Science, Institut National Polytechnique (France)

Takeo Watanabe Associate Professor of Psychology, College of Arts and Sciences. BA, MA, PhD, University of Tokyo (Japan)

Jeremy Wolfe Adjunct Professor of Cognitive and Neural Systems, College of Arts and Sciences; Professor of Ophthalmology, Harvard Medical School; Psychophysicist, Brigham & Women’s Hospital; Director of Psychophysical Studies, Center for Clinical Cataract Research. AB, Princeton University; PhD, Massachusetts Institute of Technology

Curtis E. Woodcock Professor, Department of Geography and Environment; Director of Geographic Applications, Center for Remote Sensing, College of Arts and Sciences. BA, MA, PhD, University of California, Santa Barbara

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The Department of Cognitive and Neural Systems (CNS)

The Department of Cognitive and Neural Systems (CNS) provides advanced training and research experience for graduate students and qualified undergraduates interested in the neural and computational principles, mechanisms, and architectures that underlie human and animal behavior, and the application of neural network architectures to the solution of technological problems. The department’s training and research focus on two broad questions. The first question is: How does the brain control behavior? This is a modern form of the Mind/Body Problem. The second question is: How can technology emulate biological intelligence? This question needs to be answered to develop intelligent technologies that are well suited to human societies. These goals are symbiotic because brains are unparalleled in their ability to intelligently adapt on their own to complex and novel environments. Models of how the brain accomplishes this are developed through systematic empirical, mathematical, and computational analysis in the department. Autonomous adaptation to a changing world is also needed to solve many of the outstanding problems in technology, and the biological models have inspired qualitatively new designs for applications. CNS is a world leader in developing biological models that can quantitatively simulate the dynamics of identified brain cells in identified neural circuits, and the behaviors that they control. This new level of understanding is leading to comparable advances in intelligent technology.

CNS is a graduate department that is devoted to the interdisciplinary training of graduate students. The department awards MA, PhD, and BA/MA degrees. Its students are trained in a broad range of areas concerning computational neuroscience, cognitive science, and neuromorphic systems. The biological training includes study of the brain mechanisms of vision and visual object recognition; audition, speech, and language understanding; recognition learning, categorization, and long-term memory; cognitive information processing; selforganization and development, navigation, planning, and spatial orientation; cooperative and competitive network dynamics and short-term memory; reinforcement and motivation; attention; adaptive sensory-motor planning, control, and robotics; biological rhythms; consciousness; mental disorders; and the mathematical and computational methods needed to support advanced modeling research and applications. Technological training includes methods and applications in image processing, multiple types of signal processing, adaptive pattern recognition and prediction, information fusion, and intelligent control and robotics.

The foundation of this broad training is the unique interdisciplinary curriculum of seventeen interdisciplinary graduate courses that have been developed at CNS. Each of these courses integrates the psychological, neurobiological, mathematical, and computational information needed to theoretically investigate fundamental issues concerning mind and brain processes and the applications of artificial neural networks and hybrid systems to technology. A student’s curriculum is tailored to his or her career goals with academic and research advisors. In addition to taking interdisciplinary courses within CNS, students develop important disciplinary expertise by also taking courses in departments such as biology, computer science, engineering, mathematics, and psychology. In addition to these formal courses, students work individually with one or more research advisors to learn how to carry out advanced interdisciplinary research in their chosen research areas. As a result of this breadth and depth of training, CNS students have succeeded in finding excellent jobs in both academic and technological areas after graduation.

The CNS department interacts with colleagues in several Boston University research centers and with Boston-area scientists collaborating with these centers. The units most closely linked to the department are the Center for Adaptive Systems and the CNS Technology Laboratory. CNS is also part of a major new NSF Center of Excellence for Learning in Education, Science, and Technology (CELEST); see www.cns.bu.edu/CELEST. Students interested in neural network hardware can work with researchers in CNS and at the College of Engineering. In particular, CNS is part of a major ONR MURI Center for Intelligent Biomimetic Image Processing and Classification that includes colleagues who are developing neuromorphic VLSI chips. Other research resources include the campus-wide Program in Neuroscience, which unites cognitive neuroscience, neurophysiology, neuroanatomy, neuropharmacology, and neural modeling across the Charles River Campus and the School of Medicine; in sensory robotics, biomedical engineering, computer and systems engineering, and neuromuscular research within the College of Engineering; in dynamical systems within the Department of Mathematics; in theoretical computer science within the Department of Computer Science; and in biophysics and computational physics within the Department of Physics. Key colleagues in these units hold joint appointments in CNS in order to expedite training and research interactions with CNS core faculty and students.

In addition to its basic research and training program, the department organizes an active colloquium series, various research and seminar series, and international conferences and symposia, to bring distinguished scientists from experimental, theoretical, and technological disciplines to the department.

The department is housed in its own four-story building, which includes ample space for faculty and student offices and laboratories (active perception, auditory neuroscience, computer vision/computational neuroscience, sensory-motor control, speech and language, technology, and visual psychophysics), as well as an auditorium, classroom, seminar rooms, a library, and a faculty-student lounge. The department has a powerful computer network for carrying out large-scale simulations of behavioral and brain models and applications.

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Laboratory and Computer Facilities

The department is funded by fellowships, grants, and contracts from federal agencies and private foundations that support research in life sciences, mathematics, artificial intelligence, and engineering. Facilities include laboratories for experimental research and computational modeling in visual perception; audition, speech, and language processing; sensory-motor control and robotics; and technology transfer. Data analysis and numerical simulations are carried out on a state-of-the-art computer network comprised of Sun workstations, Macintoshes, and both 32-bit and 64-bit PCs. A PC farm running BU’s own version of Linux (BU Linux v4.6 based on Fedora Core 3) is available as a distributed computational environment. All students have department-supplied PCs on their desktops (running either Microsoft Windows XP Pro or BU Linux) allowing them to run their simulations either locally or remotely on one of the department’s workstations. Mathematical simulation and modeling are carried out using standard software packages such as Mathematica or Matlab, as well as SPlus and VisSim.

The department maintains a core collection of books and journals, and has access both to the Boston University libraries and to the many other collections of the Boston Library Consortium.

In addition, several specialized facilities and software are available for use. These include:

Active Perception Laboratory Models of the visual system often examine steady-state levels of neural activity during presentations of visual stimuli. It is difficult, however, to envision how such steady-states could occur under natural viewing conditions, given that the projection of the visual scene on the retina is never stationary. The Active Perception Laboratory is dedicated to the investigation of the interactions between visual perception and behavior. Research focuses on the theoretical and computational analysis of the influences of motor activity on the sampling and representation of visual information, the coupling of models of neuronal systems with robotic systems, and the design of psychophysical experiments with human subjects. The Active Perception Laboratory includes extensive computational facilities that allow the execution of large-scale simulations of neural systems. Additional facilities include instruments for the psychophysical investigation of eye movements during visual analysis, including an accurate and non-invasive eye tracker, and robotic systems for the simulation of different types of behavior. The Active Perception Laboratory hosts “Mr. T,” a humanoid robot with two 6-degrees-of-freedom arms and a head/eye system designed to replicate visual input signals to the human eye.

Auditory Neuroscience Laboratory The Auditory Neuroscience Laboratory in the Department of Cognitive and Neural Systems (CNS) is an experimental and theoretical laboratory focused on auditory perception, particular spatial auditory perception, plasticity, and attention. The laboratory contains numerous PCs used both as workstations for students to model and analyze data and to control laboratory equipment and run experiments. The other major equipment in the laboratory includes special-purpose signal processing and sound generating equipment, electromagnetic head-tracking systems, a two-channel spectrum analyzer, and other miscellaneous equipment for producing, measuring, analyzing, and monitoring auditory stimuli. The Auditory Neuroscience Laboratory consists of three adjacent rooms in the basement of 677 Beacon Street (the home of the CNS department). One room houses an 8 ft. by 8 ft. single-walled sound-treated booth as well as space for students. The second room is primarily used as student workspace for developing and debugging experiments. The third space houses a robotic arm, capable of automatically positioning a small acoustic speaker anywhere on the surface of a sphere of adjustable radius, allowing automatic measurement of the signals reaching the ears of a listener for a sound source from different positions in space, including the effects of room reverberation.

Computer Vision/Computational Neuroscience Laboratory The Computer Vision/Computational Neuroscience Laboratory is comprised of an electronics workshop, including a surface-mount workstation, PCD fabrication tools, and an Alterra EPLD design system; an active vision laboratory including actuators and video hardware; and systems for computer-aided neuroanatomy and application of computer graphics and image processing to brain sections and MRI images. The laboratory supports research in the areas of neural modeling, computational neuroscience, computer vision, robotics, and fMRI imaging. The major question being addressed is the nature of representation of the visual world in the brain, in terms of observable neural architectures such as topographic mapping and columnar architecture. The application of novel architectures for image processing for computer vision and robotics is also a major topic of interest. Recent work in this area has included the design and patenting of novel actuators for robotic active vision systems, the design of real-time algorithms for use in mobile robotic applications, and the design and construction of miniature autonomous vehicles using space-variant active vision design principles. Recently one such vehicle has successfully driven itself on the streets of Boston. Applications of fMRI imaging to measuring the topographic structure of human primary and extra-striate visual cortex are a current focus of research.

Sensory-Motor Control Laboratory The Sensory-Motor Control Laboratory supports experimental studies of sensory-motor behavior and computational studies of neural circuits that enable learned voluntary action. Equipment includes a computer-controlled, helmet-mounted, video-based, eye-head tracking system. The latter’s camera samples eye position at 240Hz and also allows reconstruction of what subjects are attending to as they freely scan a scene under normal lighting. Thus the system affords a wide range of visuo-motor studies. To facilitate computational studies, the laboratory is connected to the Department’s and University’s extensive network of Linux and Windows workstations and Linux computational servers.

Speech and Language Laboratory The Speech Laboratory includes facilities for analog-to-digital and digital-to-analog software conversion. Ariel equipment allows reliable synthesis and playback of speech waveforms. An Entropic signal-processing package provides facilities for detailed analysis, filtering, spectral construction, and formant tracking of the speech waveform. Various large databases, such as TIMIT and TIdigits, are available for testing algorithms of speech recognition.  The laboratory also contains a network of Windows-based PC computers equipped with software for the analysis of functional magnetic resonance imaging (fMRI) data, including region-of-interest (ROI) based analyses involving software for the parcellation of cortical and subcortical brain regions in structural MRI images.

Technology Laboratory The Technology Laboratory fosters the development of neural network models derived from basic scientific research, and facilitates the transition of the resulting technologies to software and applications. The Lab was established in 2001, with a grant from the Air Force Office of Scientific Research: “Information Fusion for Image Analysis: Neural Models and Technology Development.” Current projects include multi-level fusion and data mining in a geospatial context, in collaboration with the Boston University Center for Remote Sensing; and medical image analysis, in collaboration with the Center for Biomedical Imaging at the Boston University Medical Center. This research and development effort builds on models of opponent-color visual processing, contour and texture processing, and Adaptive Resonance Theory (ART) pattern learning and recognition, as well as other models of vision, associative learning, and prediction. Additional projects include collaborations with the Harvard Medical School, to develop methods for analysis of large-scale medical databases, currently to predict HIV resistance to antiretroviral therapy; and with HRL (formerly Hughes Research Laboratories), to develop robotic platforms. Associated basic research projects are conducted within the joint context of scientific data and technological constraints. Emerging neural network technologies are embedded in the CNS Image Processing Toolkit and the CNS Neural Classifier Toolkit. Software, articles, and educational materials are available through through CELEST Technology, a growing resource for the NSF Center for Excellence for Learning in Education, Science, and Technology.

Visual Psychophysics Laboratory The Visual Psychophysics Laboratory includes a group of faculty and graduate students that conducts psychophysical and computational modeling studies of many aspects of visual perception, including motion perception, shape-from-texture, contour extraction, and visual navigation. See: http://cns.bu.edu/vislab/. The laboratory occupies an 800-square-foot suite, including three dedicated rooms for data collection, and houses a variety of computer-controlled display platforms, including Macintosh, Windows, and Linux workstations. Ancillary resources for visual psychophysics include a computer-controlled video camera, stereo viewing devices, a photometer, and a variety of display-generation, data-collection, and data-analysis software.

Affiliated Laboratories Affiliated CAS/CNS faculty members have additional laboratories ranging from visual and auditory psychophysics and neurophysiology, anatomy, and neuropsychology to engineering and chip design. These facilities are used in the context of faculty/student collaborations.

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Admissions

Prospective applicants are urged to write directly to the department at the following address: Department of Cognitive and Neural Systems, 677 Beacon Street, Boston University, Boston, MA 02215; 617-353-9481; fax: 617-353-7755; e-mail: inquiries@cns.bu.edu. A copy of the program brochure will be sent to the applicant.

To obtain application materials, write to the Admissions Office, Graduate School of Arts and Sciences, Boston University, 705 Commonwealth Avenue, Boston, MA 02215; 617-353-2696. Applications should be received by the Graduate School admissions office no later than January 15. Late applications will be considered until May 1; after that date applications will be considered only as special cases. Under certain circumstances, January admission may be possible, with an application deadline of October 15.

Applicants are required to submit undergraduate (and, if applicable, graduate) transcripts, three letters of recommendation, and Graduate Record Examination (GRE) scores. GRE scores may be waived for MA candidates and, in exceptional cases, for PhD candidates, but absence of these scores may decrease an applicant’s chances for admission and financial aid.

Completed applications are to be mailed to the Graduate School admissions office.

MA in Cognitive and Neural Systems

Course Requirements MA students are required to complete eight semester courses (32 credits), at least six of which must be from the CNS curriculum. The remaining courses may be selected, with approval of the student’s faculty advisor, from other CNS courses and from courses offered by the departments of biology, physiology, medicine, computer science, engineering, mathematics, statistics, physics, and psychology.

MA Comprehensive Examination The MA examination is offered each year in January and in May. A student must have passed at least four 500-level courses in the CNS curriculum to take the MA examination.

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PhD in Cognitive and Neural Systems

Course Requirements PhD students are required to complete at least 16 semester courses (64 credits) as follows: at least ten courses chosen from the CNS department’s curriculum, of which at least two must be 700- or 800-level courses, with the remaining courses chosen to form a coherent area of expertise. The latter courses will be selected in consultation with the student’s faculty advisor.

Students who enter the PhD program with a master’s degree in biology, physiology, medicine, computer science, engineering, mathematics, statistics, physics, or psychology are required to take eight courses (32 credits) chosen from the CNS department’s curriculum, at least two of which must be 700- or 800-level courses, and to fulfill all other program requirements.

PhD Qualifying Examinations Students are required to pass a qualifying examination on the CNS curriculum. The examination is offered each year in January and in May. A student must have passed eight courses in the CNS curriculum to take the PhD qualifying examination.

Dissertation Requirements Before finalizing dissertation plans, students are required to submit a written prospectus. A dissertation and final oral examination must be completed in accordance with the general requirements for the PhD as outlined in the front of this bulletin.

BA/MA in Biology and Cognitive and Neural Systems

The BA/MA in Biology and Cognitive and Neural Systems is an interdepartmental program in the College of Arts and Sciences and the Graduate School of Arts and Sciences. The program allows undergraduate majors in biology to begin working toward an MA in Cognitive and Neural Systems while still completing the Department of Biology BA requirements.

Admission to the BA/MA Program College of Arts and Sciences students currently in or entering the junior year are eligible to apply for admission. Students must apply before March 1 of their junior year and must meet a GPA requirement of at least 3.0 through the end of their junior year. Students admitted to the BA/MA program will typically have completed at least one CNS course. In order to be admitted into the BA/MA program, students must have completed at least Calculus I and II (MA 123 and 124, or equivalent) and Linear Algebra (MA 242). The application should include a letter from the student’s Department of Biology advisor. Application forms for admission to the BA/MA program may be obtained from the Graduate School of Arts and Sciences Office, Room 112, 705 Commonwealth Avenue, Boston, MA 02215.

Requirements Students must complete all requirements for the BA in Biology as specified in the Undergraduate Programs Bulletin; plus all requirements for the MA in Cognitive and Neural Systems, as specified in the Graduate School of Arts and Sciences Bulletin. In particular, 32 courses (128 credits) are required for the BA and 8 courses (32 credits) are required for the MA degree. In total, 40 courses (160 credits) are required. Students receive the BA and MA degrees simultaneously. Graduation applications must be submitted for both the BA and MA portions of the degree.

BA/MA in Computer Science and Cognitive and Neural Systems

The BA/MA in Computer Science and Cognitive and Neural Systems is an interdepartmental program in the College of Arts and Sciences and the Graduate School of Arts and Sciences. The program allows undergraduate majors in computer science to begin working toward an MA in Cognitive and Neural Systems while still completing the Department of Computer Science BA requirements.

Admission to the BA/MA Program College of Arts and Sciences students currently in or entering the junior year are eligible to apply for admission. Students must apply before March 1 of their junior year and must meet a GPA requirement of at least 3.0 through the end of their junior year. Students admitted to the BA/MA program will typically have completed at least one CNS course. The application should include a letter from the student’s Computer Science Department advisor. Application forms for admission to the BA/MA program may be obtained from the Graduate School of Arts and Sciences Office, Room 112, 705 Commonwealth Avenue, Boston, MA 02215.

Requirements Students are required to complete all requirements for the BA in Computer Science as specified in the Undergraduate Programs Bulletin; plus all requirements for the MA in Cognitive and Neural Systems, as specified in the Graduate School of Arts and Sciences Bulletin. In particular, 32 courses (128 credits) are required for the BA and 8 courses (32 credits) are required for the MA degree. In total, 40 courses (160 credits) are required. Students receive the BA and MA degrees simultaneously. Graduation applications must be submitted for both the BA and MA portions of the degree.

BA/MA in Mathematics and Cognitive Neural Systems

The BA/MA in Mathematics and Cognitive and Neural Systems is an interdepartmental program in the College of Arts and Sciences and the Graduate School of Arts and Sciences. The program allows undergraduate majors in mathematics to begin working toward an MA in Cognitive and Neural Systems while still completing the BA requirements in the Department of Mathematics.

Admission to the BA/MA Program College of Arts and Sciences students currently in or entering the junior year are eligible to apply for admission. Students must apply before March 1 of their junior year and must meet a GPA requirement of at least 3.0 through the end of their junior year. Students admitted to the BA/MA program will typically have completed at least one CNS course. The application should include a letter from the student’s Mathematics Department advisor. Application forms for admission to the BA/MA program may be obtained from the Graduate School of Arts and Sciences Office, Room 112, 705 Commonwealth Avenue, Boston, MA 02215.

Requirements Students are required to complete all requirements for the BA in Mathematics as specified in the Undergraduate Programs Bulletin; plus all requirements for the MA in Cognitive and Neural Systems, as specified in the Graduate School of Arts and Sciences Bulletin. In particular, 32 courses (128 credits) are required for the BA and 8 courses (32 credits) are required for the MA degree. In total, 40 courses (160 credits) are required. Students receive the BA and MA degrees simultaneously. Graduation applications must be submitted for both the BA and MA portions of the degree.

BA/MA in Psychology and Cognitive and Neural Systems

The BA/MA in psychology and cognitive and neural systems is an interdepartmental program in the College of Arts and Sciences and the Graduate School of Arts and Sciences. The program allows undergraduate majors in psychology to begin working toward an MA in cognitive and neural systems while still completing the Department of Psychology BA requirements.

Admission to the BA/MA Program College of Arts and Sciences students currently in or entering the junior year are eligible to apply for admission. Students must apply before March 1 of their junior year and must meet a GPA requirement of at least 3.0 through the end of their junior year. Students admitted to the BA/MA program will typically have completed at least one CNS course. In order to be admitted into the BA/MA program, students must have completed at least Calculus I and II (MA 123 and 124, or equivalent) and Linear Algebra (MA 242).

The application should include a letter from the student’s Department of Psychology advisor. Application forms for admission to the BA/MA program may be obtained from the Graduate School of Arts and Sciences Office, Room 112, 705 Commonwealth Avenue, Boston, MA 02215.

Requirements Students must complete all requirements for the BA in Psychology as specified in the Undergraduate Programs Bulletin; plus all requirements for the MA in Cognitive and Neural Systems, as specified in the Graduate School of Arts and Sciences Bulletin. In particular, 32 courses (128 credits) are required for the BA and 8 courses (32 credits) are required for the MA degree. In total, 40 courses (160 credits) are required. Students receive the BA and MA degrees simultaneously. Graduation applications must be submitted for both the BA and MA portions of the degree.

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Course Offerings

CAS CN 500 Computational Methods in Cognitive and Neural Systems

Prereq: one year of calculus or consent of instructor. Introduction to mathematical methods and computer simulation for modeling cognitive and neural systems. Topics include computer simulation methods, control theory, difference and differential equations, digital signal processing, image processing, optimization, statistics. Selected readings from current literature emphasize theory and applications relevant to the study of cognitive and neural systems. Rucci. 4 cr, 1st sem.

CAS CN 510 Principles and Methods of Cognitive and Neural Modeling I

Prereq: one year of calculus and consent of instructor. Explores psychological, biological, mathematical, and computational foundations of behavioral and brain modeling. Topics include organizational principles, mechanisms, local circuits, network architectures, cooperative and competitive nonlinear feedback systems, associative learning systems, and self-organizing, code-compression systems. The adaptive resonance theory model unifies many course themes. Meets with STH TX 810. Guenther. 4 cr, 1st sem.

CAS CN 520 Principles and Methods of Cognitive and Neural Modeling II

Prereq: one semester of linear algebra and consent 
of instructor. Analyzes three main traditions in models of learning: unsupervised (self-organized) learning, supervised learning (learning with a teacher), and reinforcement learning. Architectures studied include adaptive filters, backpropagation, competitive learning, self-organizing feature maps, gradient descent procedures, Boltzmann machines, simulated annealing, neocognitron, and gated dipoles. TBA. 4 cr, 2nd sem.

CAS CN 530 Neural and Computational Models of Vision

CAS CN 540 Neural and Computational Models of Adaptive Movement Planning and Control

CAS CN 550 Neural and Computational Models of Recognition, Memory, and Attention

CAS CN 560 Neural and Computational Models of Speech Perception and Production

CAS CN 570 Neural and Computational Models of Conditioning, Reinforcement, Motivation, and Rhythm

Prereq: CAS CN 510 or consent of instructor. Develops neural and computational models of how humans and animals learn to successfully predict environmental events and generate behavioral actions that satisfy internally defined criteria of success or failure. Reinforcement learning and its homeostatic (drive, arousal, rhythm) and nonhomeostatic (reinforcement) modulators are analyzed in depth. Recognition learning and recall learning networks are joined to the reinforcement learning network to analyze how these several processes cooperate to generate successful goal-oriented behavior. Maladaptive behaviors and certain mental disorders are analyzed from a unified theoretical perspective. Applications to the design of freely moving adaptive robots are noted. Rucci. 4 cr, 2nd sem.

CAS CN 580 Introduction to Computational Neuroscience

Prereq: senior standing in a natural science or Department of Mathematics or consent of instructor. This introductory level course focuses on building a background in neuroscience, but with emphasis on computational approaches. Topics include basic biophysics of ion channels, Hodgkin-Huxley theory, use of simulators such as NEURON and GENESIS, recent applications of the compartmental modeling technique, and a survey of neuronal architectures of the retina, cerebellum, basal ganglia, and neocortex. Schwartz. 4 cr, 1st sem.

GRS CN 699 Teaching College Cognitive and Neural Systems I

The goals, contents, and methods of instruction in cognitive and neural systems. General teaching-learning issues. Required of all teaching fellows. TBA. 2 cr, 1st & 2nd sem.

GRS CN 700 Computational and Mathematical Methods in Neural Modeling

Prereq: consent of instructor. Introduction to advanced computational topics used in quantitative modeling. Techniques from signal processing, probability, statistics, vector quantization, optimal control, and ordinary and partial differential equations. Theory, simulations, and techniques illustrated with neural networks and other behavioral and biological models. Cohen. 4 cr, 2nd sem.

GRS CN 710 Advanced Topics in Neural Modeling

Prereq: CAS CN 550 and consent of instructor. Examines current neural network models to prepare students to participate in research on an advanced level. Topics are chosen based upon the latest discoveries and methodologies in the field and upon the research interests of advanced CNS students. Carpenter. 4 cr, 1st sem.

GRS CN 720 Neural and Computational Models of Planning and Temporal Structure in Behavior

Prereq: CAS CN 510 or consent of instructor; CAS CN 540 also recommended. Identifies characteristics and principles of serial plan formulation, choice, and learning in humans. Includes theoretical analyses and neural network modeling of such processes as they appear in communicative speech and gesture, handwriting, typing, tool use, and object assembly. Bullock. 4 cr, 1st sem.

GRS CN 730 Models of Visual Perception

Prereq: CAS CN 530 and consent of instructor. Offers advanced survey of topics in the neural and computational modeling of psychophysical data in mammalian vision. Assignments include oral presentations on selected readings and a term paper containing a literature review and model development and analysis. Mingolla. 4 cr, 2nd sem.

GRS CN 740 Topics in Sensory Motor Control

Prereq: CAS CN 540 or consent of instructor. Topics include spatial representation, speech production, and rhythmic movement. Representations appropriate for handwriting, reaching, speaking, and walking are investigated with emphasis on different levels of representation and interactions between these levels. Material includes psychophysical data, neurophysiology, and neural models. Guenther. 4 cr, 2nd sem.

GRS CN 760 Topics in Speech Perception and Recognition

Prerequisites: CAS CN 560 or consent of instructor. This course surveys advanced topics in automatic speech recognition and auditory representation of speech signals, especially as they relate to speech perception. The course is constructed around a thorough introduction to state-of-the-art techniques in automatic speech recognition and relates these to perspectives obtained from perceptual and neurophysiological research. The course begins with the necessary fundamentals in digital signal processing and statistical pattern recognition, then discusses the major techniques in automatic speech recognition, including neural networks, hidden markov models, and dynamic programming. It explores the relation of these techniques to neurophysiological processing and psycholinguistic data, and evaluates neural models of auditory processing and speech perception. Modeling techniques, including parameter optimization and goodness-of-fit tests, are covered. Cohen. 4 cr, 1st sem.

GRS CN 780 Topics in Computational Neuroscience

Prereq: CAS MA 225 and MA 242 or consent of instructor. In this seminar, recent research papers and applications in computational neuroscience are reviewed. Topics covered include cortical modeling, analog VLSI, active perception, robotic control, stereo vision, and computer-aided neuroanatomy. Schwartz. 4 cr, 2nd sem.

GRS CN 810 Topics in Cognitive and Neural Systems: Visual Event Perception

Prereq: CAS CN 530 or consent of instructor. This course offers an advanced treatment of selected topics of current interest in the neural and computational modeling of mammalian vision. Examples of topics include visual object recognition, feature integration, computational maps, nonclassical receptive field characteristics, brightness perception, shape-from-shading, stereoscopic vision, motion perception, and optic flow. Topics vary each time the course is given. Students read primary research sources extensively and are required to present short oral critiques of selected readings to the class. A term project that combines literature review with model simulations or development of a psychophysical experiment is also required. Mingolla. 4 cr, 2nd sem.

GRS CN 811 Topics in Cognitive and Neural Systems:  Visual Perception

Prereq: consent of instructor. Problems in visual perception. Visual analyzers; visual pathways; perceptual organization; shape description; object perception; size, shape, and lightness constancy; motion perception; perceptual adaptation. 4 cr, 1st sem.

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Research in Cognitive and Neural Systems

The variable credit research courses listed below are normally open only to advanced PhD students and students engaged in sponsored research projects. Instructor’s consent required. 1st & 2nd sem.

GRS CN 911, 912 Research in Neural Networks for Adaptive Pattern Recognition
GRS CN 915, 916 Research in Neural Networks for Vision and Image Processing
GRS CN 921, 922 Research in Neural Networks for Speech and Language Processing
GRS CN 925, 926 Research in Neural Networks for Adaptive Sensory-Motor Planning and Control
GRS CN 931, 932 Research in Neural Networks for Conditioning and Reinforcement Learning
GRS CN 935, 936 Research in Neural Networks for Cognitive Information Processing
GRS CN 941, 942 Research in Nonlinear Dynamics of Neural Networks
GRS CN 945, 946 Research in Technological Applications of Neural Networks
GRS CN 951, 952 Research in Hardware Implementations of Neural Networks

Courses in Related Departments

The following courses are among those that may be useful to CNS students whose program of study includes courses outside the CNS curriculum. Other courses may be substituted with advisor’s approval. Each course is described in the Graduate School Bulletin. Except as noted, each course carries 4 credits.

Biology

Computer Science

Engineering

Biomedical Engineering

Manufacturing Engineering

Electrical, Computer, and Systems Engineering

Health Sciences

Mathematics

Medical Sciences

(Please note: The Boston University School of Medicine follows a calendar that differs from the Charles River Campus.)

Anatomy and Neurobiology

Behavioral Neuroscience

Psychology

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31 October 2007
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