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

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Neuroscience

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2021

Author dc:creator

• Gribkova, Ekaterina Dmitrievna

Contributors dc:contributor

• Gillette, Rhanor
• Gillette, Martha U
• Llano, Daniel A
• Mehta, Prashant G

• Artificial Intelligence
• Behavior
• Computational Models
• Learning
• Memory
• Synaptic Plasticity

Statement dc:rights

• Copyright 2020 Ekaterina Dmitrievna Gribkova

Language dc:language

en

Handle dc:identifier

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

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/109340