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，我们这个时代的重大科学挑战之一是从机械上理解神经元的集合如何完成这些极其复杂的功能。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。首先必须能够理解和模拟更简单的大脑，例如线虫秀丽隐杆线虫的神经系统由于其简单性，科学家们已经能够绘制出整个神经系统的物理线路——连接组——试图重建线虫大脑的生物物理特性。由于神经元类型及其产生的活动模式不同，科学家们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的生物物理特性来解决这个问题。线虫神经元并为这些神经元及其构成的回路构建精确的数学模型，目标是重现不同神经类型和神经回路的神经活动模式，并最终模拟蠕虫大脑如何产生简单的行为来实现这一目标。研究人员将采用电生理学系统方法记录线虫中 42 个选定的神经元类型，这组神经元是根据其在多项深入研究的行为回路中的已知功能而选择的，包括化学感应、机械感觉、热感觉、伤害感受电路以及下游整合和运动电路将通过对每种神经元类型的实验获得详细的电生理参数和神经动力学记录，并在对其进行全面表征后存入可供科学界使用的公共数据库。神经元，研究人员将根据基于深度强化学习（深度 RL）的新型机器学习方法对单个神经元动力学和电流进行建模。开发寻找参数候选，使它们适合方程以满足记录中的多个优化目标，随后将集成到基于连接组的全脑框架中，以开发解剖学和生物物理上正确的电路模型。这些动态模型将通过各种计算消融进行测试，这项探索性研究是一个原理验证测试案例，旨在评估生物物理单神经元模型对全脑电生理组模拟的影响并提供初步见解。最终，从该项目中获得的知识有望成为理解和建模更复杂的神经系统的基石。该奖项反映了 NSF 的法定使命，并已被授予。通过使用基金会的智力优点和更广泛的影响审查标准进行评估，认为值得支持。