US-United Kingdom Cooperative Science:  A Theoretical Study of the Perceptual Prerequisites of Learning
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{'SignBlockName': 'Pierre Perrolle', 'PO_EMAI': None, 'PO_PHON': None}
This award will support collaborative research between Dr. Andrew               Barto, University of Massachusetts, and Professor Horace Barlow,                Physiological Laboratory, Cambridge University, England.  Dr. Barto             will spend a six month sabbatical at the King's College Research                Center at Cambridge.  He will participate in the Research Center's              Program on Biological Information Processing and will collaborate               with Dr. Barlow on formulating and evaluating perceptual principles             that are linked to the facilitation of goal-directed learning.                                                                                                  The investigators propose to investigate how encoding schemes for               sensory input, depending minimally on the specific behavior to be               learned, can facilitate that learning.  They will examine a variety             of existing and novel coding principles with respect to their                   implications for goal-directed learning. They will focus                        particularly on the ideas being developed by Barlow on minimum                  entropy encoding principles.  The investigators plan to compare and             contrast these principles theoretically and, where need arises, via             computational experiments.  They will also relate their theoretical             findings to data relevant to the coding principles exhibited by                 populations of neurons.  Dr. Barto is a recognized leader in the                field of real-time computational learning problems and Dr. Barlow               has been for many years a preeminent researcher in brain                        perception.  The results of this research should make an important              contribution to understanding the theoretical link between                      biological and artificial neural networks.
{'FirstName': 'Andrew', 'LastName': 'Barto', 'PI_MID_INIT': 'G', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Andrew G Barto', 'EmailAddress': 'barto@cs.umass.edu', 'NSF_ID': '000201472', 'StartDate': '12/01/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Massachusetts Amherst', 'CityName': 'AMHERST', 'ZipCode': '010039252', 'PhoneNumber': '4135450698', 'StreetAddress': '101 COMMONWEALTH AVE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Massachusetts', 'StateCode': 'MA', 'CONGRESSDISTRICT': '02', 'CONGRESS_DISTRICT_ORG': 'MA02', 'ORG_UEI_NUM': 'VGJHK59NMPK9', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF MASSACHUSETTS', 'ORG_PRNT_UEI_NUM': 'VGJHK59NMPK9'}
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{'Code': '5946', 'Text': 'UNITED KINGDOM'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1988&All=true', 'xml': '8815252.xml'}