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- Astrocytes;Exploration;Information seeking;Reinforcement Learning (1)
- Control;Limit-Cycle Oscillators;Network Inference;Nonlinear Networks;Synchronization (1)
- Dynamical systems;encoding;gating;optimization;resource allocation;working memory (1)
- Ensemble control;Estimation Theory;Polynomial approximation;Quantum control;Quantum Estimation;Robust control (1)
- Graphons (1)
Articles 1 - 6 of 6
Full-Text Articles in Systems Science
Probing The Mechanisms Of Reinforcement Learning: Reinforcement Learning, Ventral Striatal Astrocytes, And The Dynamic Coordination Of Information Seeking With Learning, Fatih Sogukpinar
McKelvey School of Engineering Graduate Student Theses & Dissertations
While reinforcement learning has been a vital component in artificial intelligence and machine learning, there exist many open questions about its implementations and how to improve them, in both minds and machines. Among these are i) the contribution of non-neuronal cell types to reinforcement learning, and ii) information-seeking behavior during reinforcement learning. In this thesis, we studied these main topics pertaining to reinforcement learning. In the first chapter, we examined the role of astrocytes in reinforcement learning, and in the second, we investigated human information seeking during reinforcement learning. Neurons in the human and animal brain have been known to …
A Control Theoretic Approach To The Stochastic Multi-Armed Bandit Problem With Applications In Hyperparameter Optimization, Jonathan Gornet
A Control Theoretic Approach To The Stochastic Multi-Armed Bandit Problem With Applications In Hyperparameter Optimization, Jonathan Gornet
McKelvey School of Engineering Graduate Student Theses & Dissertations
Decision-making under uncertainty is a fundamental problem encountered frequently in many real-world applications. This challenge has been rigorously formulated as the Stochastic Multi-Armed Bandit (SMAB) problem, which consists of a learner interacting with an environment. For each interaction, the learner selects an action and then receives a reward from the environment based on the chosen action. The learner's objective is to maximize the accumulated reward over a set number of rounds. This thesis addresses the SMAB problem by leveraging the field of Control Theory and dynamical systems. We specifically focus on a SMAB environment where the rewards are the output …
Moment Ensemble Approaches For Estimation And Robust Quantum Control, Andre Luiz Paes De Lima
Moment Ensemble Approaches For Estimation And Robust Quantum Control, Andre Luiz Paes De Lima
McKelvey School of Engineering Graduate Student Theses & Dissertations
Large-scale population dynamics governed by ensemble systems present significant challenges due to their inherently high dimensionality. Recent advances have demonstrated the effectiveness of moment-based methods (particularly those employing polynomial bases such as Legendre and Chebyshev polynomials) when integrated with optimization techniques for control design in ensemble systems. However, these methods have not been extensively explored for enhancing robustness against systematic noise, nor have they been widely applied to complex quantum systems involving multi-parameter configurations and entanglement phenomena. In this work, we extend the application of moment methods in ensemble systems through two projects. Initially by developing a Kalman filter analogue, …
On The H-Property For Step-Graphons: Residual Case, Wanting Gao
On The H-Property For Step-Graphons: Residual Case, Wanting Gao
McKelvey School of Engineering Graduate Student Theses & Dissertations
We investigate the H-property for step-graphons. Specifically, we sample graphs Gn on n nodes from a step-graphon and evaluate the probability that Gn has a Hamiltonian decomposition in the asymptotic regime as n → ∞. It has been shown in Belabbas and Chen (2023); Belabbas et al. (2021) that for almost all step-graphons, this probability converges to either zero or one. We focus in this paper on the residual case where the zero-one law does not apply. We show that the limit of the probability still exists and provide an explicit expression of it. We present a complete proof of …
Dynamic Spectral And Systems-Theoretic Approaches For Inference And Control Of Heterogeneous Complex Networks, Bharat Kumar Singhal
Dynamic Spectral And Systems-Theoretic Approaches For Inference And Control Of Heterogeneous Complex Networks, Bharat Kumar Singhal
McKelvey School of Engineering Graduate Student Theses & Dissertations
Networks of nonlinear systems are commonly employed to describe a diverse range of phenomena across physics, engineering, neuroscience, and biology. The undesirable behaviors of such systems, in the form of neurological disorders, power grid failures, or ecological collapses, have spurred significant interest in understanding their dynamic structures and developing effective control strategies. These systems are typically large-scale, consist of heterogeneous units, and are partially observable with limited measurement data, presenting theoretical and computational challenges for control design and connectivity inference. This thesis addresses these challenges by developing novel algorithms for pattern formation in populations of stable limit-cycle oscillators and connectivity …
Synthesis Of Neuronal Network Dynamics For Optimal Stimulus Encoding And Retention, Bethanna Thompson
Synthesis Of Neuronal Network Dynamics For Optimal Stimulus Encoding And Retention, Bethanna Thompson
McKelvey School of Engineering Graduate Student Theses & Dissertations
A central goal in neuroscience is to understand the relationship between the structure and activity of the brain and the resulting experience of thought and behavior that the brain underpins. Within this goal is the specific study of working memory: the process of storing, managing, and integrating incoming sensory information in the context of ongoing tasks. The process of working memory is foundational to higher cognition and reasoning, determining how we comprehend and interact with the world around us. The neural circuits responsible for working memory are seemingly able to store and process afferent stimuli with other recent or relevant …