Multiscale theory of synapse function with model reduction by machine learning

通过机器学习进行模型简化的突触功能多尺度理论

• 批准号: 10263653
• 负责人: ERIC D MJOLSNESS
• 金额: $ 113.46万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10263653
• 关键词: 3-Dimensional; Actins; Aging; Algorithms; Alzheimer' s Disease; Astrocytes; Biochemistry; Brain; Calcium; Calcium Oscillations; Calcium Signaling; Collaborations; Communities; Computer software; Cytoskeleton; Data; Dendritic Spines; Development; Docking; Ensure; Event; Future; Goals; Graph; Growth; Head; Image; Learning; Libraries; Link; Machine Learning; Memory; Methods; Modeling; Modernization; Molecular; Morphogenesis; Motivation; Neurosciences; Pharmacologic Substance; Physics; Preparation; Process; Proteins; Reaction; Reproducibility; Research Personnel; Resolution; Shapes; Signal Pathway; Software Tools; Structure; Synapses; System; Time; Vertebral column; Work; base; calmodulin-dependent protein kinase II; computational neuroscience; data sharing; data tools; interoperability; machine learning algorithm; machine learning method; multi-scale modeling; nano; nervous system disorder; open source; particle; reconstruction; sharing platform; simulation; spatiotemporal; success; synaptic function; theories; tool

Project Summary/Abstract This project constructs a unifying model that links synaptic morphodynamics, the fundamental process of learning and memory in the brain, to the underlying molecular signaling pathways that regulate it. The motivation for this work is a new class of machine learning methods for multiscale modeling that are a promising candidate for linking the disparate spatial and temporal scales involved, from s calcium events in nano-domains to actin reorganization on the order of minutes across a dendritic spine head. Previously, it has only been possible to study each of these scales in isolation. The project brings together experts in (1) modeling the biochemistry at synapses, (2) modeling the growth of the actin cytoskeleton, and (3) developing the theory and algorithms of multiscale modeling with machine learning. The result of this collaboration will be a milestone model in cellular neuroscience that mechanistically connects calcium signaling in dendritic spines to the growth of the actin cytoskeleton in spine remodeling. Currently, there are few models that can e ectively make predictions about actin structure formation based on changes in calcium in ux into the post-synaptic spine. Since the new data-driven models will be more computationally ecient than exact simulations, it will also be possible to incorporate them into coarse-scale models of synapses used in network simulations and in neuroengineering applications. Additionally, the methods developed in this work an important contribution to modeling in cellular neuroscience, particularly because they are data-driven and therefore widely applicable. Finally, the development of a suite of software tools for multiscale modeling with machine learning will catalyze future collaborations and scienti c developments in the neuroscience community, particularly using models that aim to connect cellular phenomena with mechanisms at sub-second resolution. Such models can potentially bene t the development of pharmaceutical targets for learning de cits associated with aging and neurological disorders such as Alzheimers.

项目摘要/摘要 该项目构建了一个统一的模型，将突触形态动力学联系起来，这是 大脑中的学习和记忆，到调节它的潜在分子信号通路。动机 因为这项工作是一类新的机器学习方法，用于多尺度建模，是一个很有前途的候选方法 将不同的空间和时间尺度联系起来，从纳米领域的S钙事件到肌动蛋白 在树枝状脊椎头部数分钟内进行重组。在此之前，人们只能研究 每一个标尺都是孤立的。该项目汇集了以下领域的专家：(1)建立突触的生物化学模型， (2)建立肌动蛋白细胞骨架生长模型；(3)发展多尺度理论和算法 使用机器学习进行建模。这一合作的结果将是细胞神经科学的一个里程碑模型 它机械地将树突棘中的钙信号与脊柱中肌动蛋白细胞骨架的生长联系起来。 改建。目前，很少有模型能够有效地预测肌动蛋白结构的形成。 基于UX中钙离子进入突触后脊椎的变化。由于新的数据驱动模型将更加 在计算上不同于精确模拟，将它们合并到粗略尺度也是可能的 用于网络模拟和神经工程应用的突触模型。此外，这些方法 这项工作对细胞神经科学中的建模做出了重要贡献，特别是因为它们 是由数据驱动的，因此广泛适用。最后，开发了一套适用于多尺度的软件工具 机器学习建模将促进神经科学的未来合作和科学发展 社区，特别是使用旨在亚秒级将细胞现象与机制联系起来的模型 决议。这样的模型可能有助于开发用于学习的药物靶标 与衰老和阿尔茨海默氏症等神经疾病有关。