Improving Physiological Modeling with Machine Learning

点击翻译按钮获取中文摘要

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.