Improving Physiological Modeling with Machine Learning

通过机器学习改进生理建模

• 批准号: 2052499
• 负责人: Daniel Forger
• 金额: $ 36万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 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://www.nsf.gov/awardsearch/showAward?AWD_ID=2052499&HistoricalAwards=false
• 关键词: Improving; Physiological; Modeling; Machine; Learning

Machine learning (ML) is a crucial tool to address many societal challenges. ML is also a critical factor in American competitiveness. Much of ML was inspired by biology and sometimes aims to understand biological systems, especially the brain. Yet, the methodologies of ML resemble biological systems only superficially. Understanding biological systems is one of the central goals of Mathematical Biology (MB). MB takes a seemingly opposite approach to ML by carefully matching experimental data and reconstructing the physiology of biological systems. So far, the tools of ML and MB have been mainly distinct. In this project, the Principal Investigator (PI) will seek synergies between these two different methodologies and build mathematical tools that bridge these two approaches. PI will also develop tools to study real-world circadian rhythms and sleep. One tool is a smartphone app the PI will deploy. PI shall also work to increase the participation of underrepresented groups in mathematics through outreach to underserved high schools. The mathematical models in this project consist of ordinary differential equations (ODE) and stochastic differential equations. One of the primary goals is to build ML tools based on biophysically accurate models of neurons. These models will use the formalism developed by Hodgkin and Huxley. The key problem to be addressed here is how learning can occur with highly nonlinear models and time dependence through improvements on the backpropagation technique. Additionally, the PI will develop approaches that use predictions from currently developed models to enhance classification in ML. Key problems to address here are how to classify ODE models' simulations or incorporate uncertainty into ODE models properly. The methods will be tested against experimental data on human sleep.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

机器学习(ML)是解决许多社会挑战的重要工具。ML也是美国竞争力的关键因素。ML的大部分灵感来自生物学，有时旨在了解生物系统，特别是大脑。然而，ML的方法论只是表面上类似于生物系统。了解生物系统是数学生物学的中心目标之一。MB通过仔细匹配实验数据和重建生物系统的生理学，采取了似乎与ML相反的方法。到目前为止，ML和MB的工具主要是截然不同的。在这个项目中，首席调查员(PI)将寻求这两种不同方法之间的协同作用，并建立起连接这两种方法的数学工具。Pi还将开发工具来研究真实世界的昼夜节律和睡眠。其中一个工具是PI将部署的智能手机应用程序。国际数学联合会还应努力通过推广到服务不足的高中，增加未被充分代表的群体在数学方面的参与。本项目的数学模型由常微分方程组和随机微分方程组组成。主要目标之一是基于神经元的生物物理精确模型来构建ML工具。这些模型将使用霍奇金和赫胥黎发展的形式主义。这里要解决的关键问题是如何通过改进反向传播技术，在高度非线性模型和时间相关性的情况下进行学习。此外，PI将开发使用当前开发的模型的预测来增强ML分类的方法。这里要解决的关键问题是如何对ODE模型的模拟进行分类，或者将不确定性恰当地融入到ODE模型中。这些方法将根据人类睡眠实验数据进行测试。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

The asymmetric particle population density method for simulation of coupled noisy oscillators

耦合噪声振子模拟的非对称粒子群密度法

• DOI: 10.1016/j.jcp.2023.112157
• 发表时间: 2023
• 期刊: Journal of Computational Physics
• 影响因子: 4.1
• 作者: Wang, Ningyuan;Forger, Daniel B.
• 通讯作者: Forger, Daniel B.

The Level Set Kalman Filter for State Estimation of Continuous-Discrete Systems

用于连续离散系统状态估计的水平集卡尔曼滤波器

• DOI: 10.1109/tsp.2021.3133698
• 发表时间: 2022
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Wang, Ningyuan;Forger, Daniel B.
• 通讯作者: Forger, Daniel B.