자료유형  

 학위논문

Control Number  

0017162029

International Standard Book Number  

9798384239017

Dewey Decimal Classification Number  

001

Main Entry-Personal Name  

McCreesh, Michael Patrick Durrell.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of California, San Diego., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

160 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

General Note  

Advisor: Cortes, Jorge.

Dissertation Note  

Thesis (Ph.D.)--University of California, San Diego, 2024.

Summary, Etc.  

요약The brain, composed of billions of interconnected neurons, forms a complex network that exhibits an incredibly wide array of behaviors. Treating this structure as a dynamical system provides a multitude of tools to use to model and understand the relationship between structure and function in the brain. As subnetworks exist at all levels in the brain, ranging from networks of individual neurons to networks of entire regions, a diverse set of models, each with different properties have been considered to study different dynamic brain behaviors. One such class of models are firing rate models, which monitor the average spike rate of populations of neurons. A particular firing rate model is the linear-threshold network model, which exhibits a wide range of rich behaviors based on the underlying network structure and inputs. This ability to exhibit a variety of behaviors motivates the use of this model to study a variety of dynamical behaviors observed in the brain.This dissertation considers three problems within the realm of modeling dynamical brain behaviors with the linear-threshold model. First, motivated by the appearance of oscillatory behavior when observing brain activity, we study the existence of oscillations in the linear-threshold dynamics. In order to provide sufficient conditions for oscillations in specific network topologies we also provide conditions for the stability of equilibrium points that maintain a specific support. Second, we discuss the dynamical brain behavior of selective inhibition and recruitment. We consider thalamocortical networks with both hierarchical and star-connected topologies, and focus on how the inclusion of the thalamus can improve the stabilizability properties of the linear-threshold dynamics relevant to the application. We finish by investigating the problem of reference tracking for the linear-threshold dynamics, which can be used to frame many brain behaviors. We approach this both analytically and with a data-driven approach to better match observations on how the brain processes information.

Subject Added Entry-Topical Term  

Systems science.

Subject Added Entry-Topical Term  

Applied mathematics.

Subject Added Entry-Topical Term  

Mathematics.

Index Term-Uncontrolled  

Linear-threshold model

Index Term-Uncontrolled  

Network dynamics

Index Term-Uncontrolled  

Dynamical brain behaviors

Index Term-Uncontrolled  

Data-driven approach

Added Entry-Corporate Name  

University of California, San Diego Mechanical and Aerospace Engineering

Host Item Entry  

Dissertations Abstracts International. 86-03B.

Electronic Location and Access  

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Control Number  

joongbu:656906