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AISB opportunities Bulletin Item



The Boston University Department of Cognitive and Neural Systems (CNS)
offers comprehensive graduate training 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.
Applications for Fall 2009 admission and financial aid are now being
accepted for PhD, MA, and BA/MA degree programs.
For program details, please see the CNS Brochure and 
Graduate School of Arts & Sciences Bulletin
On-line applications may be submitted via . 
Paper applications may be downloaded from

Please address questions about the application process to:
Mr. Robin Amos (

Applications for admission and financial aid should be received in the 
Graduate School Admissions Office by December 15, 2008, but will be 
given full consideration if received by January 15, 2009. Late applications 
will be considered until April 15, 2009; after that date applications 
will be considered only as special cases.

Applicants are required to submit undergraduate (and, if applicable,
graduate) transcripts, three letters of recommendation, a personal
statement, Graduate Record Examination (GRE) general test scores, 
and TOEFL test scores (when applicable).
Non-degree students may also enroll in CNS courses on a part-time basis.

Please see for a complete listing 
of department faculty, staff and affiliates. 


Please see for a complete listing 
of graduate courses offered by 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 departments 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 producing comparable advances in intelligent technology.

CNS is a graduate department that is devoted to the interdisciplinary training of graduate students. The department offers MA, PhD, and BA/MA degree programs. 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; self-organization 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 students 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. Also 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 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 (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.

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 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:

Auditory Neuroscience Laboratory 
The Auditory Neuroscience Laboratory 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 from a sound source from different positions in space, including the effects of room reverberation. Current information about the Auditory Neuroscience Laboratory can be found at

Computer Vision and Computational Neuroscience Laboratory
The Computer Vision and Computational Neuroscience Laboratory consists 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. Current information about the Speech Laboratory can be found at

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 laboratory 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 the CELEST Technology Website (, a growing resource for the NSF Center of 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: 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.

Department of Cognitive and Neural Systems
Boston University
677 Beacon Street
Boston, MA 02215
Phone: 617/353-9481
Fax: 617/353-7755