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Synthesizing the Connectome Developm...

Bihn, Michael Joseph.

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Synthesizing the Connectome Development of the Infantile Human Brain.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Synthesizing the Connectome Development of the Infantile Human Brain./
Author:	Bihn, Michael Joseph.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	211 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31296186
ISBN:	9798382583815

Synthesizing the Connectome Development of the Infantile Human Brain.
Bihn, Michael Joseph.

Synthesizing the Connectome Development of the Infantile Human Brain. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 211 p.

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Thesis (Ph.D.)--University of Colorado Colorado Springs, 2024.

Neuroscience is utilizing new methods such as Diffusion Tensor Imaging (DTI) with structural MRI data to define brain growth with higher accuracy. We anticipate that with time their tools and processing will provide insights not yet seen. We anticipate more of these advances in the near future.{A0}In the ongoing research effort of synthesizing sentience into artificial intelligence, we propose a modular network that emulates neurological synaptic evolution in the neonate brain. Our hypothesis is that if one were to successfully develop a synthesized emulation of human's six-month hippocampus as it initializes adult-like glucose usage and synaptic density which is generally accepted in the domain of neuroscience as being the foundation of human sentience, then so can human sentience be injected into the synthesized replication of said six-month hippocampus.Accordingly, we present a theoretical proposition that facilitates a significant step towards overcoming the commonsense challenge that state-of-the-art artificial intelligence systems are still grappling with today; where even the most powerful artificial intelligence systems are void of the common sense of a three year old: That lemons are sour, that things fall towards the ground and that they, as children, can pretend to be somebody else. Herein, we present a methodology to efficiently promulgate the research goal of integrating sentience and common sense reasoning into artificial intelligence, taking a neurological rather than a psychological approach.With the division of a priori and a posteriori knowledge, we accept a prior as without experience and a posteriori as with experience. Without actual brain lobes and communication between them, experiential events cannot be learned. Hence, we look to the fetal development of the brain as a priori, before experiences in the world. We note that it is known that a fetus can hear vibrations and move, which is an experience, thus showing an overlap of the a priori and a posteriori To define brain growth with a mathematical representation we must start at conception as the DNA builds the brain, known to neuroscience as the transcriptome. But for our consideration, lacking any fetal MRI data, we will start with the data at one month and end at twenty-four months of age. We take a computational approach rather than a genetic approach to derive a mathematical model of infantile brain growth from one to twenty-four months.In this dissertation, we lay the foundational theory for a mathematical definition of brain development. The purpose of which defines how a machine would deploy resources to specific brain functions over time to emulate the child's learning of common sense. These functions include the development of both gray and white matter of the brain over time. Our mathematical definitions provide the resulting effects of the growth forces, and may be utilized to characterize differences in individual development.{A0}This work developed a methodology for analyzing and processing time series data into a longitudinal mathematical model of growth. Our initial motivation was infantile brain growth. This same methodology could also be applied to any time series data to develop a longitudinal mathematical model for whatever the scientist is studying. While we chose to apply this methodology to a human brain problem, it's application is not necessarily limited to human biology.

ISBN: 9798382583815Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

MRI data

Synthesizing the Connectome Development of the Infantile Human Brain.
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520 $a Neuroscience is utilizing new methods such as Diffusion Tensor Imaging (DTI) with structural MRI data to define brain growth with higher accuracy. We anticipate that with time their tools and processing will provide insights not yet seen. We anticipate more of these advances in the near future.{A0}In the ongoing research effort of synthesizing sentience into artificial intelligence, we propose a modular network that emulates neurological synaptic evolution in the neonate brain. Our hypothesis is that if one were to successfully develop a synthesized emulation of human's six-month hippocampus as it initializes adult-like glucose usage and synaptic density which is generally accepted in the domain of neuroscience as being the foundation of human sentience, then so can human sentience be injected into the synthesized replication of said six-month hippocampus.Accordingly, we present a theoretical proposition that facilitates a significant step towards overcoming the commonsense challenge that state-of-the-art artificial intelligence systems are still grappling with today; where even the most powerful artificial intelligence systems are void of the common sense of a three year old: That lemons are sour, that things fall towards the ground and that they, as children, can pretend to be somebody else. Herein, we present a methodology to efficiently promulgate the research goal of integrating sentience and common sense reasoning into artificial intelligence, taking a neurological rather than a psychological approach.With the division of a priori and a posteriori knowledge, we accept a prior as without experience and a posteriori as with experience. Without actual brain lobes and communication between them, experiential events cannot be learned. Hence, we look to the fetal development of the brain as a priori, before experiences in the world. We note that it is known that a fetus can hear vibrations and move, which is an experience, thus showing an overlap of the a priori and a posteriori To define brain growth with a mathematical representation we must start at conception as the DNA builds the brain, known to neuroscience as the transcriptome. But for our consideration, lacking any fetal MRI data, we will start with the data at one month and end at twenty-four months of age. We take a computational approach rather than a genetic approach to derive a mathematical model of infantile brain growth from one to twenty-four months.In this dissertation, we lay the foundational theory for a mathematical definition of brain development. The purpose of which defines how a machine would deploy resources to specific brain functions over time to emulate the child's learning of common sense. These functions include the development of both gray and white matter of the brain over time. Our mathematical definitions provide the resulting effects of the growth forces, and may be utilized to characterize differences in individual development.{A0}This work developed a methodology for analyzing and processing time series data into a longitudinal mathematical model of growth. Our initial motivation was infantile brain growth. This same methodology could also be applied to any time series data to develop a longitudinal mathematical model for whatever the scientist is studying. While we chose to apply this methodology to a human brain problem, it's application is not necessarily limited to human biology.
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