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{ "pk": 31684, "title": "Integrating Learning into Models of Human Memory: The Hebbian Recurrent Network", "subtitle": null, "abstract": "We develop an interactive model of human mem-\nory called the Hebbian Recurrent Network ( HRN )\nwhich integrates work in the mathematical modeling\nof memory with that in error correcting connection-\nist networks. It incorporates the Matrix Model (Pike,\n1984) into the Simple Recurrent Network (SRN, El-\nman, 1989). The result is an architecture which has\nthe desirable memory characteristics of the matrix\nmodel such as low interference and massive general-\nization, but which is able to learn appropriate en-\ncodings for items, decision criteria and the control\nfunctions of memory which have traditionally been\nchosen a priori in the mathematical memory litera-\nture. Simulations demonstrate that the HRN is well\nsuited to a recognition task inspired by typical mem-\nory peiradigms. In comparison to the SRN , the HRN\nis able to learn longer lists, and is not degraded sig-\nnificantly by increasing the vocabulary size.", "language": "eng", "license": { "name": "", "short_name": "", "text": null, "url": "" }, "keywords": [], "section": "Submitted Presentations", "is_remote": true, "remote_url": "https://escholarship.org/uc/item/0k24d99r", "frozenauthors": [ { "first_name": "Simon", "middle_name": "", "last_name": "Dennis", "name_suffix": "", "institution": "The University of Queensland", "department": "" }, { "first_name": "Janet", "middle_name": "", "last_name": "Wiles", "name_suffix": "", "institution": "The University of Queensland", "department": "" } ], "date_submitted": null, "date_accepted": null, "date_published": "1993-01-01T18:00:00Z", "render_galley": null, "galleys": [ { "label": "PDF", "type": "pdf", "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/31684/galley/22752/download/" } ] }