Logical Computation Associative Storage and Self            Organization in Neural Networks
{'Code': '07010000', 'Directorate': {'LongName': 'Directorate For Engineering'}, 'Division': {'LongName': 'Div Of Electrical, Commun & Cyber Sys'}}
{'SignBlockName': 'Sarah A. Clark'}
Neuro-anatomical models of brain function have proved fertile ground            in the development of efficient computational structures in recent              times.   Keynotes of these structures include a high degree of                  parallelism, distributed storage of information, robustness and fault-          tolerance, and the usage of very simple processing elements                     individually  performing tasks of low computational complexity.                                                                                                 These characteristics of neural networks lend themselves to a plethora          of engineering problems.  Applications include (i) image and signal             processing, and particularly inthe areas of robot and computer vision,          and automatic speech generation and recognition, (ii) associative and           content-addressable memories with applications to a variety of areas            including expert systems utilizing large data bases, automated                  directory assistance, and coding and decoding systems, and (iii)                adaptive systems capable of self-organization and "learning."  These            applications utilize fully the high degree of parallelism and the               distributed format of inormation storage in these networks.  Rapid              convergence results typically, as a consequence of the highly parallel          operation.  In addition, the distributed storage of informationresults          in a robust, fault-tolerant system.  This is a particularly appealing           feature for systems in hostile environments, and for applications               where constant monitoring is unfeasible as in space-borne systems.              Another feature useful in practice is the utilization of very simple            computational elements as the basic component of these networks.  This          augurs well for the possibility of low cost, mass production of these           networks for specific applications.                                                                                                                             The area has a particularly rich inter-disciplinary flavour.                    Engineering analysis, architecture design, and development of                   materials for these networks exist symbiotecally with investigations            in artificial intelligence and parallel computing in computer science,          spin glasses in statistical physics, and modelling of neural phenomena          in neurobiology.
{'FirstName': 'Santosh', 'LastName': 'Venkatesh', 'EmailAddress': 'venkates@ee.upenn.edu', 'StartDate': '07/15/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Pennsylvania', 'CityName': 'Philadelphia', 'ZipCode': '191046205', 'PhoneNumber': '2158987293', 'StreetAddress': 'Research Services', 'CountryName': 'United States', 'StateName': 'Pennsylvania', 'StateCode': 'PA'}
{'Code': '1517', 'Text': 'ELECT, PHOTONICS, & MAG DEVICE'}
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