| 1994 — 1997 |
Carpenter, Gail [⬀] |
N/AActivity Code Description:
No activity code was retrieved: click on the grant title for more information |
Neural Networks For Stable Disbributed Coding and Prediction @ Trustees of Boston University
9401659 Carpenter The proposed research will design, develop, analyze, and apply neural network architectures for stable recognition coding and prediction. One set of projects will focus on new unsupervised ART (Adaptive Resonance Theory) and supervised ARTMAP systems to support distributed recognition codes that are stable under training regimes with either fast or slow learning. Typically, the pattern of activity of a recognition code is distributed when there is little reason to select one category over another, as might occur early in a learning process. This pattern becomes more compressed as categories are more sharply defined through experience. Generalization and storage capacity of the ART neural networks will be greatly increased by the distributed coding capability. Related work will focus on the computational requirements of distributed search and on spatio-temporal recognition problems.
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0.915 |
| 2004 — 2011 |
Grossberg, Stephen (co-PI) [⬀]
Carpenter, Gail (co-PI) [⬀]
Mingolla, Ennio [⬀]
Stanley, H. Eugene (co-PI) [⬀]
Hasselmo, Michael (co-PI) [⬀]
Miller, Earl
Shinn-Cunningham, Barbara (co-PI) [⬀] |
N/AActivity Code Description:
No activity code was retrieved: click on the grant title for more information |
Slc Center: Celest: a Center For Learning in Education, Science, and Technology @ Trustees of Boston University
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.
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0.915 |
