Stephen Grossberg - US grants

The project will develop real-time neural network models for the adaptive control and planning of complex sensory-motor skills. Such models are equally important for understanding human movement planning and the design of flexible characterized circuits for generating synchronous movements of multi-joint motor systems, such as arms and speech articulators, at variable speeds. The current research will investigate the design of supplementary circuits that are capable of rapidly adapting to the unexpected loads and inertias that arise during novel movements, and of slowly learning to predict the correct movement parameters when these novel movements become familiar through practice. In addition, the project will attemp to analyze how elementary, or atomic, movements are organized into complex movement sequences, and how the controls for these sequences become automated through learning. Also, eye-hand coordination will be analyzed by showing how a representation of where the eye-head system is looking is associatively transformed into where the hand-arm system is commanded to move. Issues concerning the learned timing of actions and self-organization of a body-centered coordinated system will also be analyzed.

Support is requested for an international conference on the subject of neural networkds. This subject deals with the modeling of behavioral and brain processes, and the application of neural modeling concepts to technological problems. Those topics have provided much stimulus in research areas such as vision and image processing, sensory-motor control and robotics, associative learning, self-organization, and cognitive information processing. The Conference is sponsored by the International Neural Network Society, which comprises researchers from diverse disciplines (engineering, psychology, neuroscience, mathematics, computer science, and physics). A principal goal of the meeting is to encourage students and younger faculty to learn about and contribute to the field; the NSF funds will be used to provide travel grants for them.

This project is being funded through the Learning and Intelligent Systems (LIS) Initiative. The project aims to advance understanding of how the brain generates intelligent behavior by examining the capacity to think about sequences of events. Whether cooking an elaborate meal or merely dialing a phone number, multiple events in a specific temporal order must be kept in mind, in a form of working memory that helps in planning complex thoughts and actions. This problem will be studied in the project through an interdisciplinary approach. To directly probe brain mechanisms, neurophysiological experiments will be performed on awake behaving animals. Computer-based experiments with young children will be used to discover how children learn sequential behaviors and to test how to optimize such learning. Behavioral studies will be done on how human infants learn sequences. Cognitive and neural modeling will be used to discover brain designs and mechanisms to link the animal neurophysiological data to the human cognitive data. This interdisciplinary approach promises to produce insights that are beyond the scope of any single approach. Conducting experiments in two different animal species (monkeys and rats), in human infants, and in young children will permit identification of intelligent mechanisms that are preserved across different species. The animal studies will analyse the activity of large groups of single neurons to study how multiple neurons interact to generate intelligent behaviors. Neural modeling will probe the laws that govern these behaviors, and will make predictions that link brain to behavior. Through these analyses of how we learn and remember sequences of events, a foundation will be built for studying the neural basis of high-level cognitive operations such as planning and reasoning; for developing better educational software; and for applying models towards the solution of outstanding technological problems that require algorithms which emulate human intelligence.

This grant would enable the annual International Conference on Cognitive and Neural Systems to continue on May 24-27, 2000 and in May, 2001. The conference focuses on the two general themes: How Does the Brain Control Behavior? and How Can Technology Enable Biological Intelligence? The conference would also emphasize Learning and Intelligent Systems. Many conferences are covering cognitive or neurobiological data. A few conferences are covering cognitive or neurological models, but not both. Yet other conferences are covering artificial neural networks and other neuromorphic applications . The Cognitive and Neural Systems conference is unique in covering both cognitive and neural experiments and models, as well as a range of intelligent applications, and doing so within a conference program with a single program track. The meeting was prepared to do this because this is precisely what the CNS graduate program at Boston Universitiy was created to do. The conference will include invited tutorials and lectures, and contributed lectures and posters by experts on the biology and technology of how the brain and other intelligent systems adapt to a changing world. The conference is aimed at researchers and students of computational neuroscience, connectionists cognitive science, artificial neural networks, neuromorphic engineering, and artificial intelligence.

Science and technology of the twenty first century will be dominated by computing. The training of well prepared computational scientists and engineers demands an approach that transcends the strictures of the standard academic curriculum. This IGERT project will provide students who follow discipline-specific studies with a multidisciplinary program of training in computational science that will maximize their exposure to the cross-disciplinary nature of computational science and to its realm of industrial applications. This program will build a three-way interface between graduate students, the academic faculty training them, and industrial, government, and academic research laboratories who may ultimately be their employers. It will offer training opportunities through a program of visits and internships at such institutions. As the students proceed towards a Ph.D. within their chosen field, they will also earn a certificate in computational science in recognition of the special expertise that they have developed. The students will have access to state-of-the-art computational facilities at the University and will participate in multidisciplinary seminars, workshops and regular meetings with faculty and students in this program. They will also attend specialized lectures on ethics and issues of law that are relevant for scientists in the computational field. Special efforts will be made to enhance the participation of students from groups underrepresented in the sciences. The IGERT project will have the merit of providing a large number of doctoral students pursuing disciplinary research with broad education on the methods and scope of application of computational science, through the teamwork of faculty from many different departments. The results of this interdisciplinary project will be widely disseminated through seminars, workshops and web publishing and thus it will have the broad impact of establishing a model for training in computational science students who are pursuing education in a variety of disciplinary fields.

IGERT is an NSF-wide program intended to meet the challenges of educating U.S. Ph.D. scientists and engineers with the multidisciplinary backgrounds and the technical, professional, and personal skills needed for the career demands of the future. The program is intended to catalyze a cultural change in graduate education by establishing innovative new models for graduate education and training in a fertile environment for collaborative research that transcends traditional disciplinary boundaries. In the fifth year of the program, awards are being made to twenty-one institutions for programs that collectively span the areas of science and engineering supported by NSF.

The Center of Excellence for Learning in Education, Science, and Technology (CELEST) brings together leading scientists, educators, and technologists from Boston University, Brandeis University, Massachusetts Institute of Technology, and the University of Pennsylvania to study real-time autonomous learning systems by integrating experimental and computational brain science, biologically inspired technology, and classroom innovation. Contributing scientists are drawn from four Boston University Departments and the Center for Adaptive Systems, the Center for Memory and Brain, the Science and Mathematics Education Center, the Hearing Research Center, and the Center for Polymer Studies; the Brandeis University Department of Psychology and the Volen Center for Complex Systems; the MIT Department of Brain and Cognitive Sciences, the Picower Center for Learning and Memory, and the Harvard/MIT Speech and Hearing Bioscience and Technology Program; and the University of Pennsylvania Department of Psychology.
Intellectual Merit and Creative Concepts: CELEST brings together educators, scientists, and technologists to carry out four types of mutually reinforcing and integrated activities: (1) quantitative behavioral and brain modeling of both normal and abnormal learning processes during perception, cognition, emotion, and action; (2) interdisciplinary cognitive and neuroscience experiments to probe these processes and to test model predictions; (3) development of algorithms, based on biological learning models, for incremental fast learning about complex and rapidly changing environments in large-scale engineering and technological applications that are important in many areas of society; and (4) integration of research and education through contributions to educational technology, curriculum development, and early career recruitment of underrepresented communities into scientific practice. These goals are achieved through interactions among eight main Thrusts in:
Learning in visual perception and recognition: laminar cortical dynamics of adaptive behavior;
Learning in audition, speech, and language; learning in cognitive-emotional interactions and planned sequential behaviors;
Learning and episodic memory: encoding and retrieval;
Learning in concept formation and rule discovery;
Learning in attentive recognition and neuromorphic technology;
Educational technology, curriculum development, and outreach; and
Diversity outreach.
Broader Impact: CELEST will foster interdisciplinary collaborations and training across all its units: frequent seminars, workshops, colloquia, conferences, and publications; integration of research and education by translating basic science results into interdisciplinary curriculum development; and web-based and hands-on training for teachers and students, including classroom activities with a national and international impact. CELEST will hereby provide world-class expertise towards advancing key SLC program goals; namely, the psychological and pedagogical aspects of learning, the biological basis of learning, machine learning, learning technologies, and mathematical analyses and modeling of them all.

Major themes of the International Conference on Cognitive and Neural Systems (ICCNS) are: How does the brain control behavior? How can technology emulate biological intelligence? The conference is aimed at researchers and students in experimental and theoretical cognitive science and neuroscience, neural networks, computer science, neuromorphic engineering, artificial intelligence, mathematics, and physics. It includes invited lectures by distinguished scientists and engineers (with good representation of female scholars), and contributed lectures and posters by students and experts on the biology and technology of how the brain and other intelligent systems adapt to a changing world. There is an emphasis on enabling young scientists and students to present their work.