2026-09-20T20:27:45Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/109340 2023-07-11 col_2142_5131 col_2142_13007 com_2142_5130 com_2142_13006

Gillette, Rhanor Gillette, Rhanor Gillette, Martha U Llano, Daniel A Mehta, Prashant G Gribkova, Ekaterina Dmitrievna 2021-03-05T21:36:49Z 2021-03-05T21:36:49Z 2020-09-22 2020-12 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. Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-03-04 without embargo terms The student, Ekaterina Gribkova, accepted the attached license on 2020-09-18 at 13:54. The student, Ekaterina Gribkova, submitted this Dissertation for approval on 2020-09-18 at 15:58. This Dissertation was approved for publication on 2020-09-22 at 08:52. DSpace SAF Submission Ingestion Package generated from Vireo submission #15815 on 2021-03-04 at 15:33:57 Made available in DSpace on 2021-03-05T21:36:49Z (GMT). No. of bitstreams: 3 GRIBKOVA-DISSERTATION-2020.pdf: 9555252 bytes, checksum: 374d42516c39e839a924a55ff27a3268 (MD5) LICENSE.txt: 4215 bytes, checksum: 254e3f225300a2d1be8c971f82fddfa6 (MD5) PROQUEST_LICENSE.txt: 4561 bytes, checksum: e4239b3560df6fc19f88c6008f94766e (MD5) Previous issue date: 2020-09-22 application/pdf http://hdl.handle.net/2142/109340 en Copyright 2020 Ekaterina Dmitrievna Gribkova Artificial Intelligence Behavior Computational Models Learning Memory Synaptic Plasticity Biologically inspired computational neural models for motivated behavior, learning, and memory text Thesis Neuroscience Program Neuroscience University of Illinois at Urbana-Champaign Dissertation Ph.D.