Doborjeh, MaryamSmith, BrianSzyszka, PaulGarnell, Saul2026-03-302026-03-302026http://hdl.handle.net/10292/20825Affective Neuroscience is a field that explores the neural mechanisms underlying emotions and affective (proto-emotions) states. Seeking behavior, one of the seven affects proposed by Jaak Panksepp, plays a vital role in exploration, learning, and decision-making. This thesis investigates the neural mechanisms that underlie this enigmatic drive through an integrative approach that combines principles from various domains of science, including Comparative Neuroanatomy, Computational Affective Neuroscience, and study of dynamic systems such as Loosely Coupled Oscillators. By examining homologous brain structures across species, with a particular interest in those regions that might plausibly be involved in the neural processes associated with seeking behavior, and employing computational models in neuroscience, this research aims to shed light on the complex interplay between neural activity and affective responses. My work seeks to elucidate plausible mechanisms governing SEEK (capitalized in this context), ultimately enhancing our understanding of brain function and providing insights into the neural basis of emotional responses. By simulating neuronal motifs and circuits within the mid-brain and basal brain systems of the fruit fly, this thesis aims to provide insights into brain functions related to SEEK. This work contributes to the ongoing discourse on Cognitive Neuroscience and Artificial Intelligence (AI) by employing analytical and computational tools of neuro-science and signal analysis to bridge the explanatory gap in understanding of SEEK and seeking behavior. This thesis employs a novel computational framework to identify stable oscillatory points in pairs of excitatory and inhibitory neurons in the fan-shaped body of Drosophila, aiding in the pathfinding and testing of neural circuits associated with SEEK behavior. The study’s key achievement is the identification of these stable points, revealing dynamic neural interactions and contributing to the understanding of neural oscillations in the fan-shaped body’s architecture. The framework is efficient and reproducible for future studies. Additionally, the research explores the significance of SEEK in cognition, utilizing Drosophila’s central nervous system to probe the neurophysiological mechanisms underlying SEEK behavior. The thesis also demonstrates how Spiking Neural Network models support the investigation of SEEK and affective neuroscience. Lastly, it examines how SEEK’s relationship to Wilson-Cowan-like motifs and behavior can be applied to the Free Energy Principle and Bayesian Inference, providing in-sights into brain function and decision-making. The future direction of the thesis states that further experimental validation is needed to strengthen these findings. By advancing our knowledge of seeking behavior, this research could have far-reaching implications for various fields such as robotics, artificial intelligence, psychology, and neuroscience. By creating more accurate models of emotional processes in AI systems, we can develop machines that exhibit adaptability, efficiency, and human-like decision-making capabilities. This work's findings could pave the way for a new generation of AI systems capable of understanding and responding to emotional stimuli in a more nuanced manner, thereby improving their ability to function effectively in com-plex, dynamic environments.enThesisOpenAccess