Multiscale Model of Cerebral Blood Flow Control: Application to Small Vessel Disease and Cortical Spreading Depression

An in-time delivery of oxygen-rich blood into areas of high metabolic demand is pivotal in proper functioning of the brain and neuronal health. This highly precise communication between neuronal activity and cerebral blood flow (CBF) is termed as neurovascular coupling (NVC) or functional hyperemia. NVC is disrupted in major pathological conditions including Alzheimer’s disease, dementia, small vessel pathologies (SVD) and cortical spreading depression. Despite the utmost importance of NVC, its underlying mechanisms are not fully understood. This dissertation presents a multiscale mathematical modeling framework for studying unresolved mechanisms of NVC with major focus on K+ ions as a mediator of this process. To this end, models of single-cell electrophysiology are developed for endothelial (EC) and smooth muscle (SMC) cells of capillaries and parenchymal arterioles (PAs). Cells are electrically coupled, and large-scale geometrically-accurate models of microvascular networks are constructed. Model simulations predict an important role of capillary inward rectifying potassium channels (Kir) to sense neuronally-induced changes in extracellular potassium concentrations ([K+]o) and conduct hyperpolarizing signals over long distances to upstream PAs. Simulation results demonstrate a “tug-of-war” dynamic between Kir and voltage-gated potassium (Kv) channels in determining the Vm and myogenic tone of PA SMCs during NVC in SVD. Results also predict a key role of Kir channels in the experimentally observed multiphasic vascular response during high elevations of [K+]o in cortical spreading depression. The multiscale models presented in this study were able to accurately capture several experimentally observed responses during NVC and provided insights into their potential underlying mechanisms in health and disease. These models provide a theoretical platform where macroscale, tissue-level responses can be related to microscale, single-cell signaling pathways

A Tale of Two Approaches: Comparing Top-Down and Bottom-Up Strategies for Analyzing and Visualizing High-Dimensional Data

The proliferation of high-throughput and sensory technologies in various fields has led to a considerable increase in data volume, complexity, and diversity. Traditional data storage, analysis, and visualization methods are struggling to keep pace with the growth of modern data sets, necessitating innovative approaches to overcome the challenges of managing, analyzing, and visualizing data across various disciplines. One such approach is utilizing novel storage media, such as deoxyribonucleic acid~(DNA), which presents efficient, stable, compact, and energy-saving storage option. Researchers are exploring the potential use of DNA as a storage medium for long-term storage of significant cultural and scientific materials. In addition to novel storage media, scientists are also focussing on developing new techniques that can integrate multiple data modalities and leverage machine learning algorithms to identify complex relationships and patterns in vast data sets. These newly-developed data management and analysis approaches have the potential to unlock previously unknown insights into various phenomena and to facilitate more effective translation of basic research findings to practical and clinical applications. Addressing these challenges necessitates different problem-solving approaches. Researchers are developing novel tools and techniques that require different viewpoints. Top-down and bottom-up approaches are essential techniques that offer valuable perspectives for managing, analyzing, and visualizing complex high-dimensional multi-modal data sets. This cumulative dissertation explores the challenges associated with handling such data and highlights top-down, bottom-up, and integrated approaches that are being developed to manage, analyze, and visualize this data. The work is conceptualized in two parts, each reflecting the two problem-solving approaches and their uses in published studies. The proposed work showcases the importance of understanding both approaches, the steps of reasoning about the problem within them, and their concretization and application in various domains

異種の不揮発性メモリで構成される半導体ストレージシステムに関する研究

【学位授与の要件】中央大学学位規則第4条第1項【論文審査委員主査】竹内　健　（中央大学理工学部教授）【論文審査委員副査】山村　清隆（中央大学理工学部教授）、築山　修治（中央大学理工学部教授）、首藤　一幸（東京工業大学大学院情報理工学研究科准教授）博士（工学）中央大

Implementing Bayesian Inference with Neural Networks

Embodied agents, be they animals or robots, acquire information about the world through their senses. Embodied agents, however, do not simply lose this information once it passes by, but rather process and store it for future use. The most general theory of how an agent can combine stored knowledge with new observations is Bayesian inference. In this dissertation I present a theory of how embodied agents can learn to implement Bayesian inference with neural networks. By neural network I mean both artificial and biological neural networks, and in my dissertation I address both kinds. On one hand, I develop theory for implementing Bayesian inference in deep generative models, and I show how to train multilayer perceptrons to compute approximate predictions for Bayesian filtering. On the other hand, I show that several models in computational neuroscience are special cases of the general theory that I develop in this dissertation, and I use this theory to model and explain several phenomena in neuroscience. The key contributions of this dissertation can be summarized as follows: - I develop a class of graphical model called nth-order harmoniums. An nth-order harmonium is an n-tuple of random variables, where the conditional distribution of each variable given all the others is always an element of the same exponential family. I show that harmoniums have a recursive structure which allows them to be analyzed at coarser and finer levels of detail. - I define a class of harmoniums called rectified harmoniums, which are constrained to have priors which are conjugate to their posteriors. As a consequence of this, rectified harmoniums afford efficient sampling and learning. - I develop deep harmoniums, which are harmoniums which can be represented by hierarchical, undirected graphs. I develop the theory of rectification for deep harmoniums, and develop a novel algorithm for training deep generative models. - I show how to implement a variety of optimal and near-optimal Bayes filters by combining the solution to Bayes' rule provided by rectified harmoniums, with predictions computed by a recurrent neural network. I then show how to train a neural network to implement Bayesian filtering when the transition and emission distributions are unknown. - I show how some well-established models of neural activity are special cases of the theory I present in this dissertation, and how these models can be generalized with the theory of rectification. - I show how the theory that I present can model several neural phenomena including proprioception and gain-field modulation of tuning curves. - I introduce a library for the programming language Haskell, within which I have implemented all the simulations presented in this dissertation. This library uses concepts from Riemannian geometry to provide a rigorous and efficient environment for implementing complex numerical simulations. I also use the results presented in this dissertation to argue for the fundamental role of neural computation in embodied cognition. I argue, in other words, that before we will be able to build truly intelligent robots, we will need to truly understand biological brains

New Developments in Renewable Energy

Renewable energy is defined as the energy which naturally occurs, covers a number of sources and technologies at different stages, and is theoretically inexhaustible. Renewable energy sources such as those who are generated from sun or wind are the most readily-available and possible solutions to address the challenge of growing energy demands in the world. Newer and environmentally friendly technologies are able to provide different social and environmental benefits such as employment and decent environment. Renewable energy technologies are crucial contributors to world energy security, reduce reliance on fossil fuels, and provide opportunities for mitigating greenhouse gases. International public opinion indicates that there is strong support for a variety of methods for solving energy supply problems, one of which is utilizing renewable energy sources. In recent years, countries realized that that the renewable energy and its sector are key components for greener economies