A C. elegans whole-brain digital twin

近几十年来，大脑研究取得了显著进展。然而，神经回路的动力学，它们对动物行为的规范，对环境或内部状态的适应，以及个体之间的可变性，仍然知之甚少。为了整合神经元功能、回路级计算和全脑协调，对自由行为的动物进行全脑成像是必不可少的。虽然对大多数动物来说，这项技术是可行的，而且在一毫米长的线虫C. elegans中成熟得很快。尽管相对简单，秀丽隐杆线虫是一种行为自由的动物，为了生存、觅食和躲避捕食，它可以做出决定、学习、忘记、适应不断变化的环境，并参与集体行为。像所有的动物一样，它会发育、睡眠和衰老，它的研究已经证明它是神经生物学、神经遗传学、学习、可塑性和行为的神经基础以及神经变性的强大模型系统。虽然许多秀丽隐杆线虫神经元的功能已被广泛研究，但理解更大回路的动力学带来了新的挑战：全脑成像提供了对神经元活动的基本观察，但不能观察神经元之间的相互作用。因此，我们认为，要获得对细胞、回路和全球大脑水平的综合理解，需要机制和解释模型。这种模型必须考虑到神经回路中出现的全脑活动，这是由动物的连接组指定的。为了实现这一目标，我们的总体目标是建立秀丽隐杆线虫大脑的第一个数字双胞胎。数字孪生是现实世界系统的软件表示，用作模型来预测、解释或控制系统在不同条件下的反应。虽然通常应用于工程资产，但方法和挑战（特别是对内部工作的有限访问和对输出的有限观察）表明了与全脑数据建模的重要共性。具体目标包括：人工智能：开发人工智能工具，以受秀丽隐杆线虫连接体约束的全脑活动数据为基础，训练数字双胞胎。利用50只动物的全脑活动数据，应用、测试和扩展个体动物全脑模型的优化方法。利用学习高维时间序列（即神经活动轨迹）的低维表示的深度神经模型来增强全脑数据并引导我们的优化方法。为了统一我们的框架，以获得具有相似神经元激活模式和行为编码的模型动物集群的解决方案家族。使用概率和人口密度工具，开发和应用基于数据集人口的新型人工智能工具来训练模型人口。数字双胞胎：以细胞分辨率开发秀丽隐杆线虫大脑的生物学基础机制模型。以秀丽隐杆线虫的神经生物学为基础，实现神经元和电路模型，例如保守和可变的连接组，已知的突触极性，双边对称性等。根据数据测试和评估优化模型，并基于生物学现实实现成功解决方案的后选择机制。将成功的模型应用于模拟，为验证实验和未来研究提供新的假设，重点是理解分布式编码及其灵活性、适应性和可变性。如果成功，数字双胞胎将改变我们对秀丽隐杆线虫大脑的理解，从而改变其他动物的神经系统。这个项目将投入人工智能工具，使我们更接近这个目标。新的人工智能，以及人工智能、模拟和复杂数据的集成，将有利于生命和工程科学领域其他数字孪生的建设。

Brain research has witnessed remarkable advances in recent decades. And yet, the dynamics of neural circuits, their specification of an animal's behaviours, adaptation to context or internal state, and variability across individuals, remain poorly understood. To integrate neuronal function, circuit-level computation, and brain-wide coordination, whole-brain imaging in freely-behaving animals is essential. While daunting in most animals this technology is available and fast-maturing in the mm-long nematode, C. elegans.Despite its relative simplicity, C. elegans is a freely behaving animal that makes decisions, learns, forgets, adapts to ever-changing conditions, and engages in collective behaviour, in order to survive, forage for food and escape predation. Like all animals, it develops, sleeps and ages, and its study has proved it a powerful model system for neurobiology, neurogenetics, the neural basis of learning, plasticity and behaviour, and neurodegeneration.While the functions of many C. elegans neurons have been studied extensively, understanding the dynamics of larger circuits poses new challenges: whole-brain imaging provides essential observation of neuronal activity, but not the interactions between neurons. We therefore argue that to obtain an integrated understanding at cellular, circuit and global-brain levels requires mechanistic and explanatory models. Such models must account for brain-wide activity that emerges from the neural circuitry, as specified by an animal's connectome. To address this goal, our overall aim is to build the first digital twin of the C. elegans brain.A digital twin is a software representation of a real-world system, used as a model to predict, explain or control the system's response under different conditions. While commonly applied to engineering assets, the methodology, and the challenges (in particular, limited access to the internal working and limited observables of the outputs) suggest important commonalities with whole-brain modelling from data.Specific objectives include:AI: To develop AI tools to train a digital twin, based on whole-brain-activity data constrained by the C. elegans connectome.To apply, test and extend optimisation methods for whole-brain models of individual animals, using brain-wide activity data for >50 animals.To augment whole-brain-data and bootstrap our optimisation methods using deep neural models that learn low-dimensional representations of high-dimensional time-series (i.e. neural activity traces).To unify our framework in order to obtain families of solutions representing clusters of model animals with similar neuronal activation patterns and behavioural encoding.To develop and apply novel AI tools for training populations of models based on populations of datasets, using probabilistic and population density tools.Digital Twin: To develop biologically-grounded mechanistic models of the C. elegans brain, at cellular resolution.To implement neuronal and circuit models with appropriate grounding in C. elegans neurobiology, e.g. the conserved and variable connectome, known synaptic polarities, bilateral symmetry, etc.To test and evaluate optimised models against data and implement post-selection mechanisms for successful solutions, based on biological realism.To apply successful models in simulations to derive predictions for validation experiments and new hypotheses for future research, with focus on understanding distributed encoding and its flexibility, adaptability and variability.If successful, a digital twin will transform our understanding of the C. elegans brain, and hence, the nervous systems of other animals. This project, will put in place AI tools that bring us closer to this goal. The novel AI, and the integration of AI, simulations and complex data, will benefit the construction of other digital twins, across life and engineering sciences.