CRCNS Research Proposal: Collaborative Research: Electrophysiome: comprehensive recording and integrated modeling of the C. elegans nervous system

CRCNS 研究提案：合作研究：电生理组：线虫神经系统的全面记录和集成建模

• 批准号: 2113120
• 负责人: Cornelia Bargmann
• 金额: $ 38.2万
• 依托单位: Rockefeller University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2113120&HistoricalAwards=false
• 关键词: CRCNS; Research; Proposal; Collaborative; Electrophysiome

The integrated function of the human brain allows every individual human to have unique thoughts, perceptions, memories, and actions. One of the grand scientific challenges of our time is to mechanistically understand how collections of neurons accomplish these incredibly sophisticated functions. However, it turns out, that this is a daunting task that requires a comprehensive understanding of a brain at every level of complexity, from molecules to neurons, the circuits and systems they form, and the underlying computational principles. To reach the goal of understanding the brain, we must first be able to understand and simulate simpler brains like the nervous system of the nematode worm Caenorhabditis elegans. Given its simplicity, scientists have been able to map the physical wiring of the entire nervous system – the connectome – in the attempt to reconstruct the worm brain. However, without knowing the biophysical properties of the diverse neuron types and the activity pattern they produce, scientists have been unable to generate a unifying model that explains how the brain of this simple worm works. This project aims to address this problem by comprehensively characterizing the biophysical properties of a large portion of C. elegans neurons and constructing accurate mathematical models for these neurons and the circuits they constitute. The goal is to reproduce neural activity patterns in different neuron types and neural circuits, and eventually simulate how the worm brain generates simple behaviors. To accomplish this goal, the researchers will take a systematic approach of recording from 42 selected neuron types in C. elegans using electrophysiology. This set of neurons was selected based on their known function in multiple well-studies behavioral circuits including chemosensory, mechanosensory, thermosensory, nociceptive circuits, and downstream integrating and motor circuits. Detailed electrophysiological parameters, and recordings of neural dynamics will be obtained from experiments for each neuron type and deposited into a public database available for the scientific community. Following the comprehensive characterization of these neurons, the researchers will model the single neuron dynamics and currents according to the Hodgkin-Huxley equations. Novel machine learning methodology based on Deep Reinforcement Learning (Deep RL) will be developed to find parameter candidates such that they fit the equations to satisfy multiple optimized objectives in the recordings. Optimal single neuron models will subsequently be integrated into a connectome-based whole-brain framework to develop anatomically and biophysically correct circuit models. The robustness of these dynamic models will be tested with various computational ablations. This exploratory study is a proof-of-principle test case to evaluate the impact of biophysical single neuron models on the full-scale whole-brain electrophysiome simulation and provide initial insights into the level of abstraction possible for systemic modeling of the entire C. elegans nervous system. Ultimately, the knowledge gained from this project is expected to act as steppingstone for understanding and modeling more complex nervous systems.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.

人类大脑的综合功能使每个人都有独特的思想，感知，记忆和行动。我们这个时代最大的科学挑战之一就是从机械上理解神经元的集合是如何完成这些难以置信的复杂功能的。然而，事实证明，这是一项艰巨的任务，需要全面了解大脑的各个复杂程度，从分子到神经元，它们形成的电路和系统，以及基本的计算原理。为了达到理解大脑的目标，我们必须首先能够理解和模拟更简单的大脑，比如线虫的神经系统。由于其简单性，科学家们已经能够绘制整个神经系统的物理布线-连接体-试图重建蠕虫的大脑。 然而，由于不知道不同神经元类型的生物物理特性及其产生的活动模式，科学家们一直无法生成一个统一的模型来解释这种简单蠕虫的大脑如何工作。该项目旨在通过全面表征大部分C的生物物理特性来解决这个问题。elegans神经元，并为这些神经元及其构成的电路构建精确的数学模型。目标是在不同的神经元类型和神经回路中再现神经活动模式，并最终模拟蠕虫大脑如何产生简单的行为。 为了实现这一目标，研究人员将采用系统的方法记录C. elegans线虫using运用electrophysiology电生理学.这组神经元是基于它们在多个充分研究的行为回路中的已知功能而选择的，所述行为回路包括化学感觉回路、机械感觉回路、温度感觉回路、伤害感受回路以及下游整合和运动回路。详细的电生理参数和神经动力学记录将从每种神经元类型的实验中获得，并存入科学界可用的公共数据库。在对这些神经元进行全面表征之后，研究人员将根据Hodgkin-Huxley方程对单个神经元动力学和电流进行建模。将开发基于深度强化学习（Deep RL）的新型机器学习方法，以找到候选参数，使它们符合方程，以满足记录中的多个优化目标。最佳的单神经元模型随后将被整合到基于连接体的全脑框架中，以开发解剖学和生物病理学正确的电路模型。这些动态模型的鲁棒性将通过各种计算消融进行测试。这项探索性研究是一项原理验证测试案例，旨在评估生物物理单神经元模型对全尺寸全脑电生理组模拟的影响，并为整个C系统建模的抽象水平提供初步见解。线虫的神经系统最终，从该项目中获得的知识有望成为理解和建模更复杂神经系统的垫脚石。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。