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.

项目概要/摘要 该项目构建了一个连接突触形态动力学的统一模型，突触形态动力学是突触形态动力学的基本过程。 大脑中的学习和记忆，以及调节它的潜在分子信号传导途径。动机 这项工作是一类用于多尺度建模的新型机器学习方法，是一个有前途的候选方法 用于连接所涉及的不同空间和时间尺度，从纳米域中的钙事件到肌动蛋白 整个树突棘头部的重组大约需要几分钟。以前只能学习 这些尺度中的每一个都是孤立的。该项目汇集了以下方面的专家：(1) 模拟突触的生物化学， (2) 模拟肌动蛋白细胞骨架的生长，(3) 开发多尺度的理论和算法 使用机器学习进行建模。此次合作的成果将成为细胞神经科学的里程碑模型 机械地将树突棘中的钙信号传导与树突棘中肌动蛋白细胞骨架的生长联系起来 重塑。目前，能够有效预测肌动蛋白结构形成的模型还很少 基于进入突触后脊柱的钙的变化。由于新的数据驱动模型将更加 计算效率比精确模拟高，也可以将它们合并到粗尺度中 用于网络模拟和神经工程应用的突触模型。此外，方法 这项工作对细胞神经科学建模做出了重要贡献，特别是因为它们 是数据驱动的，因此广泛适用。最后，开发了一套用于多尺度的软件工具 机器学习建模将促进神经科学领域未来的合作和科学发展 社区，特别是使用旨在将细胞现象与亚秒级机制联系起来的模型 解决。这种模型可能有利于学习缺陷药物靶标的开发 与衰老和阿尔茨海默氏症等神经系统疾病有关。