CAREER: From Connectome to Behavior: Computational Models of Multifunctional Neural Circuits in C. elegans

职业：从连接组到行为：线虫多功能神经回路的计算模型

• 批准号: 2345446
• 负责人: Eduardo Izquierdo
• 金额: $ 60万
• 依托单位: Rose-Hulman Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2024-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2345446&HistoricalAwards=false
• 关键词: CAREER; Connectome; Behavior; Computational; Models

How an animal flexibly coordinates multiple behaviors as a cohesive unit is one of the central problems of neuroscience; multifunctionality has also been recognized as one of the fundamental challenges in the development of a general artificial intelligence. Although the ability of neural circuits to flexibly reconfigure is widespread among organisms, most studies of the neural basis of behaviors focus on isolated circuits and individual behaviors. Studies that consider multifunctional circuitry tend to focus on the switching between distinct patterns of activity, with little insight into multifunctional sensorimotor integration. With the increasing amount of anatomical, physiological and behavioral data being generated, a computational modeling framework to understand the neural basis of behavior is essential. The goal of this project is to model multiple neural circuits that have been identified in isolation and to integrate them into a single model to better understand how multifunctionality arises in sensory-driven behavioral circuits. This project is an important step toward the long-term goal of developing a behaviorally-functional brain-body-environment model of a living organism at the level of individual neurons. The cross-disciplinary methodologies developed from this project will serve as a springboard for understanding multifunctional circuits in living organisms as well as for generating artificial systems capable of robustly and efficiently performing multiple functions. The project focuses specifically on modeling and analyzing the circuits responsible for the wide range of spatial orientation behaviors in the nematode Caenorhabditis elegans. This model organism is a uniquely qualified target for integrated computational modeling of a complete animal because of the breadth of information known about its genetics, development, anatomy, and behavior. Despite this substantial knowledge, information about the electrophysiological properties of its nervous system is less complete. The project aims to constrain the model by what is known from the anatomy and physiology of the organism with reasoned simplifications about its body and environment. Then, stochastic optimization will be used to fill in electrophysiological unknowns such that the model produces behavior that matches what has been observed, including the effect of neural manipulations on behavior. The result of optimization will not be a unique model, but rather an ensemble of models that are consistent with current knowledge of the system. Each of these possibilities represents a testable hypothesis for C. elegans. The next step in the project will be to analyze the structure of this ensemble to formulate the key experiments that can distinguish between the various classes of possibilities in the worm. The results of such experiments can then be used as additional constraints for subsequent optimizations in an iterative cycle of model refinement. Besides the generation of experimentally-testable predictions that are specific to C. elegans, through the analysis of the ensemble of models, the project aims to discover general principles for how multifunctional circuits operate in living organisms more broadly.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.

动物如何灵活地协调多种行为作为一个有凝聚力的单位是神经科学的核心问题之一；多功能性也被认为是通用人工智能发展的基本挑战之一。尽管神经回路灵活重组的能力在生物体中广泛存在，但大多数关于行为的神经基础的研究都集中在孤立回路和个体行为上。考虑多功能电路的研究倾向于关注不同活动模式之间的切换，而对多功能感觉运动整合的了解很少。随着解剖学、生理学和行为学数据的不断增加，一个计算建模框架来理解行为的神经基础是必不可少的。这个项目的目标是对已经被孤立识别的多个神经回路进行建模，并将它们整合到一个单一的模型中，以更好地理解多功能性是如何在感觉驱动的行为回路中产生的。该项目是朝着在单个神经元水平上开发生物体行为功能脑-体-环境模型的长期目标迈出的重要一步。从该项目中开发的跨学科方法将作为理解活生物体中多功能电路的跳板，以及生成能够稳健有效地执行多种功能的人工系统。该项目特别侧重于建模和分析负责秀丽隐杆线虫广泛空间定向行为的电路。由于对其遗传、发育、解剖和行为的广泛了解，这种模式生物是对完整动物进行综合计算建模的唯一合格目标。尽管有大量的知识，但关于其神经系统的电生理特性的信息还不太完整。该项目旨在通过对生物体的解剖学和生理学进行合理的简化来约束该模型。然后，随机优化将用于填补电生理的未知，使模型产生与观察到的行为相匹配的行为，包括神经操纵对行为的影响。优化的结果将不是一个独特的模型，而是与系统当前知识一致的模型的集合。每一种可能性都代表了秀丽隐杆线虫的可检验假设。该项目的下一步将是分析这个集合的结构，以制定能够区分蠕虫中各种可能性的关键实验。这样的实验结果可以用作模型细化迭代周期中后续优化的附加约束。除了生成针对秀丽隐杆线虫的可实验测试的预测外，通过对模型集合的分析，该项目旨在发现更广泛的生物体中多功能电路如何运作的一般原理。该奖项反映了美国国家科学基金会的法定使命，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。