Mathematical Sciences: Analysis of Neural Networks: Stable  Category Formation In A Complex Input Environment
{'Code': '03040100', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}
{'SignBlockName': 'Michael Steuerwalt'}
CARPENTER                                                                       The investigator intends to develop and analyze models of neural systems        networks.  These models will be designed to carry out a self-organizing         pattern recognition and category learning.  As formal architectures, the        models will be translated into machine designs.                                                                                                                 As neural networks, the systems will be based on psychological and              physiological data and will be used to analyze the brain and behavior in        the process of pattern recognition and category learning.  From a               mathematical point of view, these models are based on nonlinear,                singular, high dimensional systems of ordinary differential equations.                                                                                          Network design is constrained by the conflicting requirements of                stability and adaptability, and by the complexity.  Given an arbitrary          complicated sequence of inputs, the system must be able to extract              stable categories, without a teacher.  However, the system must also            remain plastic, open to new learning.  New network designs will                 generalize systems which have been shown to stably encode arbitrary             lists of binary input patterns in such a way that, after an initial             phase, each input directly activates its category representation.  The          search mechanism is automatically disengaged once the learning is               complete.  The more general system will encode graded input patterns,           and be embedded in network hierarchies adapted to the particular                constraints of natural and artificial vision and speech.                                                                                                        Research described in this project is highly cross-disciplinary, as it          involves the neural networks science, communications, some aspects of           robotics and computer vision, neural cognitive processes, and                   mathematics.
{'FirstName': 'Gail', 'LastName': 'Carpenter', 'EmailAddress': 'gail@cns.bu.edu', 'StartDate': '03/15/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Northeastern University', 'CityName': 'BOSTON', 'ZipCode': '021155005', 'PhoneNumber': '6173733004', 'StreetAddress': '360 HUNTINGTON AVE', 'CountryName': 'United States', 'StateName': 'Massachusetts', 'StateCode': 'MA'}
[{'Code': '1266', 'Text': 'APPLIED MATHEMATICS'}, {'Code': '1517', 'Text': 'EPMD-ElectrnPhoton&MagnDevices'}, {'Code': '6840', 'Text': 'ROBOTICS'}, {'Code': '6856', 'Text': 'ARTIFICIAL INTELL & COGNIT SCI'}]
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1986&All=true', 'xml': '8611959.xml'}