{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/109340"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/109340","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Biologically inspired computational neural models for motivated behavior, learning, and memory","abstract":"The fields of artificial intelligence (AI) and machine learning have vastly expanded in the past decade, with a variety of modern applications, ranging from computer vision to language processing and medical diagnostics. While the majority of AI applications involve data classification, detection, and predictive modeling, fewer studies have explored the creation of motivated autonomous agents. The integration of neurobiological principles into AI, such as mechanisms involved in dopaminergic reward learning circuits, has been crucial for advancing more natural and biologically plausible forms of AI. The goal of this thesis is to introduce a set of biologically inspired models for motivated behavior, learning, and memory, that can be incorporated into artificially intelligent agents and networks. These models may also provide insights into the biological processes of episodic memory, aesthetics, and complex cognitive processes, as well as their evolution.","abstract_html":"The fields of artificial intelligence (AI) and machine learning have vastly expanded in the past decade, with a variety of modern applications, ranging from computer vision to language processing and medical diagnostics. While the majority of AI applications involve data classification, detection, and predictive modeling, fewer studies have explored the creation of motivated autonomous agents. The integration of neurobiological principles into AI, such as mechanisms involved in dopaminergic reward learning circuits, has been crucial for advancing more natural and biologically plausible forms of AI. The goal of this thesis is to introduce a set of biologically inspired models for motivated behavior, learning, and memory, that can be incorporated into artificially intelligent agents and networks. These models may also provide insights into the biological processes of episodic memory, aesthetics, and complex cognitive processes, as well as their evolution.","abstract_has_math":false,"creators":["Gribkova, Ekaterina Dmitrievna"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Neuroscience","degree_department":null,"school":null,"contributors":["Gillette, Rhanor","Gillette, Martha U","Llano, Daniel A","Mehta, Prashant G"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-03-05T21:36:49Z","date_published":"2021-03-05T21:36:49Z","updated_at":"2026-07-22T22:24:50Z","subjects":["Artificial Intelligence","Behavior","Computational Models","Learning","Memory","Synaptic Plasticity"],"languages":["en"],"rights":["Copyright 2020 Ekaterina Dmitrievna Gribkova"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/109340","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Gillette, Rhanor","Gillette, Martha U","Llano, Daniel A","Mehta, Prashant G"]},{"key":"dc:creator","label":"Author","values":["Gribkova, Ekaterina Dmitrievna"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-03-05T21:36:49Z","2020-09-22","2020-12"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Neuroscience"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Artificial Intelligence","Behavior","Computational Models","Learning","Memory","Synaptic Plasticity"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Ekaterina Dmitrievna Gribkova"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/109340"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The fields of artificial intelligence (AI) and machine learning have vastly expanded in the past decade, with a variety of modern applications, ranging from computer vision to language processing and medical diagnostics. 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