University of Illinois Urbana-Champaign

Biologically inspired computational neural models for motivated behavior, learning, and memory

Gribkova, Ekaterina Dmitrievna

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https://hdl.handle.net/2142/109340

Description

Title
Biologically inspired computational neural models for motivated behavior, learning, and memory
Author(s)
Gribkova, Ekaterina Dmitrievna
Issue Date
2020-09-22
Director of Research (if dissertation) or Advisor (if thesis)
Gillette, Rhanor
Doctoral Committee Chair(s)
Gillette, Rhanor
Committee Member(s)
  • Gillette, Martha U
  • Llano, Daniel A
  • Mehta, Prashant G
Department of Study
Neuroscience Program
Discipline
Neuroscience
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
  • Artificial Intelligence
  • Behavior
  • Computational Models
  • Learning
  • Memory
  • 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.
Graduation Semester
2020-12
Type of Resource
Thesis
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http://hdl.handle.net/2142/109340
Copyright and License Information
Copyright 2020 Ekaterina Dmitrievna Gribkova

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