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Title:Biologically inspired computational neural models for motivated behavior, learning, and memory
Author(s):Gribkova, Ekaterina Dmitrievna
Director of Research:Gillette, Rhanor
Doctoral Committee Chair(s):Gillette, Rhanor
Doctoral Committee Member(s):Gillette, Martha U; Llano, Daniel A; Mehta, Prashant G
Department / Program:Neuroscience Program
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Artificial Intelligence
Computational Models
Synaptic Plasticity
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.
Issue Date:2020-09-22
Rights Information:Copyright 2020 Ekaterina Dmitrievna Gribkova
Date Available in IDEALS:2021-03-05
Date Deposited:2020-12

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