The evolution of fundamental neural circuits for cognition in silico
Despite decades of research on intelligence and fundamental components of cognition, we still know very little about the structure and functionality of nervous systems. Questions in cognition and intelligent behavior are addressed by scientists in the fields of behavioral biology, neuroscience, psychology, and computer science. Yet it is difficult to reverse engineer observed sophisticated intelligent behaviors in animals and even more difficult to understand their underlying mechanisms. In this dissertation, I use a recently-developed neuroevolution platform -called Markov brain networks - in which Darwinian selection is used to evolve both structure and functionality of digital brains. I use this platform to study some of the most fundamental cognitive neural circuits: 1) visual motion detection, 2) collision-avoidance based on visual motion cues, 3) sound localization, and 4) time perception. In particular, I investigate both the selective pressures and environmental conditions in the evolution of these cognitive components, as well as the circuitry and computations behind them. This dissertation lays the groundwork for an evolutionary agent-based method to study the neural circuits for cognition in silico.
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- In Collections
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Electronic Theses & Dissertations
- Copyright Status
- In Copyright
- Material Type
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Theses
- Authors
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Tehrani-Saleh, Ali
- Thesis Advisors
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Adami, Christoph
- Committee Members
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Banzhaf, Wolfgang
Hintze, Arend
McAuley, J. Devin
Ofria, Charles
- Date Published
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2021
- Subjects
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Artificial life
Cognition
Evaluation
Evolutionary computation
Neural circuitry
Nervous system
Behavioral assessment
- Program of Study
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Computer Science - Doctor of Philosophy
- Degree Level
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Doctoral
- Language
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English
- Pages
- xvii, 150 pages
- ISBN
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9798538136780
- Permalink
- https://doi.org/doi:10.25335/81sq-g484