Sensory Neural Networks for Advanced Photodetectors
{'Code': '05010300', 'Directorate': {'Abbreviation': 'CSE', 'LongName': 'Direct For Computer & Info Scie & Enginr'}, 'Division': {'Abbreviation': 'CCF', 'LongName': 'Division of Computing and Communication Foundations'}}
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This project focuses on implementing arrays of photodetectors and               associated first-level sensory neural networks in monolithic integrated         circuit format for purposes of applying the field of vision to                  engineering applications of advanced photodetectors and for producing a         physical model that can be used in the study of multiplicative lateral          inhibition in visual systems.  The photodetectors and electronic                circuitry are fabricated using gallium-arsenide technology to allow for         upward compatibility with integrated optoelectronic systems and to              allow the constructed detector assemblies to function in harsher                operating environments.  Additionally, the monolithic integration of an         array of photodetectors with a first level neural network offers                advantages in size, reliability, cost, and hence commercial                     applicability.                                                                                                                                                  Biological visual systems perform many demanding tasks that are far             from the present capabilities of electronic hardware.  The success of           many biological systems can mostly be attributed to a large degree of           pre-processing of the visual image at the most peripheral level of the          sensory system.  This pre-processing has been described by many models,         one of which is multiplicative lateral inhibition, which has the                advantage of simple implementation in electronic circuitry and is               studied in this project.  Since recognition is studied in a fully               parallel context, advanced photonic sensors based upon this developing          technology have the potential to approach the spatial recognition               performance of biological retinas and to greatly surpass them in                temporal performance of feature acquisition.
[{'FirstName': 'Robert', 'LastName': 'Darling', 'PI_MID_INIT': 'B', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Robert B Darling', 'EmailAddress': 'bdarling@ee.washington.edu', 'NSF_ID': '000347533', 'StartDate': '07/01/1989', 'EndDate': None, 'RoleCode': 'Principal Investigator'}, {'FirstName': 'Robert', 'LastName': 'Pinter', 'PI_MID_INIT': 'B', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Robert B Pinter', 'EmailAddress': 'pinter@ee.washington.edu', 'NSF_ID': '000335633', 'StartDate': '07/01/1989', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}]
{'Name': 'University of Washington', 'CityName': 'SEATTLE', 'ZipCode': '981951016', 'PhoneNumber': '2065434043', 'StreetAddress': '4333 BROOKLYN AVE NE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Washington', 'StateCode': 'WA', 'CONGRESSDISTRICT': '07', 'CONGRESS_DISTRICT_ORG': 'WA07', 'ORG_UEI_NUM': 'HD1WMN6945W6', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF WASHINGTON', 'ORG_PRNT_UEI_NUM': None}
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{'Code': '4715', 'Text': 'COMPUTER SYSTEMS ARCHITECTURE'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1988&All=true', 'xml': '8822121.xml'}