자료유형  

 학위논문

Control Number  

0016933175

International Standard Book Number  

9798379620363

Dewey Decimal Classification Number  

616

Main Entry-Personal Name  

Desai-Chowdhry, Paheli.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of California, Los Angeles., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(182 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

General Note  

Advisor: Savage, Van M.

Dissertation Note  

Thesis (Ph.D.)--University of California, Los Angeles, 2023.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약Neurons are connected by complex branching processes that collectively process information for organisms to respond to their environment. Classifying neurons according to differences in structure or function is a fundamental piece of neuroscience. Here, by constructing new biophysical theory and testing against our empirical measures of branching structure, we establish a correspondence between neuron structure and function as mediated by principles such as time or power minimization for information processing as well as spatial constraints for forming connections. Based on these principles, we use Lagrange multipliers to predict scaling ratios for axon and dendrite sizes across branching levels. We test our predictions for radius scale factors against those extracted from images, measured for species that range from insects to whales. Notably, our findings reveal that the branching of axons and peripheral nervous system neurons is mainly determined by time minimization, while dendritic branching is mainly determined by power minimization. Further comparison of different dendritic cell types reveals that Purkinje cell dendrite branching is constrained by material costs while motoneuron dendrite branching is constrained by conduction time delay. We extend this model to incorporate asymmetric branching, where there are multiple different paths from the soma to the synapses and thus multiple interpretations of conduction time delay; one considers the optimal path and the other considers the sum of all possible paths, leading to different predictions. We find that the data for motoneurons show a distinction between the asymmetric and symmetric branching junctions, corresponding to predictions using different interpretations of the time-delay constraint. Moreover, the more asymmetric branching junctions are localized near the synapses, indicating that different functional principles affect the structure at different regions of the cell. Finally, we use machine-learning methods to classify cell types using functionally relevant structural parameters derived from our model. Incorporating branching level as a feature in classification in addition to parameters related to information flow improves performance across methods, suggesting that information flow drives localized differences in morphology. Future directions of this work include estimating specific parameters related to functional tradeoffs and myelination using numerical optimization and analyzing changes across stages of development. 

Subject Added Entry-Topical Term  

Neurosciences.

Subject Added Entry-Topical Term  

Biophysics.

Subject Added Entry-Topical Term  

Applied mathematics.

Index Term-Uncontrolled  

Biological scaling theory

Index Term-Uncontrolled  

Lagrange multipliers

Index Term-Uncontrolled  

Machine learning

Index Term-Uncontrolled  

Neuron branching

Index Term-Uncontrolled  

Neuron morphology

Added Entry-Corporate Name  

University of California, Los Angeles Biomathematics 0121

Host Item Entry  

Dissertations Abstracts International. 84-12B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:642389