A C. elegans whole-brain digital twin

线虫全脑数字双胞胎

• 批准号: BB/Z514317/1
• 负责人: Netta Cohen
• 金额: $ 32.86万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FZ514317%2F1
• 关键词: C.; elegans; whole; brain; digital

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.

近几十年来，大脑研究取得了显着进步。然而，神经回路的动态，它们对动物行为的规范，对上下文或内部状态的适应以及各个个体的可变性，仍然对知识较低。为了整合神经元功能，电路级计算和整个大脑协调，自由言论动物的全脑成像是必不可少的。尽管大多数动物在大多数动物中令人生畏，但在MM长的线虫中，秀丽隐杆线虫都可以使用和快速起作用。像所有动物一样，它也会发展，睡眠和年龄，并且它的研究证明了它是神经生物学，神经源性，学习，可塑性和行为的神经基础以及神经退行性的强大模型系统。许多秀丽秀素神经元的功能已广泛地研究了许多秀丽的extression neyur crocking ne New Active and Newurn the New挑战的动态：整个挑战的动力学：整个挑战的动力学。神经元。因此，我们认为，要在细胞，电路和全球脑水平上获得综合的理解需要机械和解释模型。这样的模型必须考虑到由动物连接组指定的神经回路出现的脑部活动。为了解决这一目标，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。一个数字双胞胎是现实世界系统的软件表示，用作在不同条件下预测，解释或控制该系统响应的模型。 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 > 50只动物的大脑全部活动数据。要使用深层神经模型来增加全脑数据并引导我们的优化方法，这些模型学习高维时序列的低维表示（即神经活动痕迹）。统一性的框架，以获取代表模型的动物的范围的框架，以培训模型的动物群体，以开发类似的神经动物和行为的神经元素。基于数据集的种群，使用概率和人口密度工具。数字双胞胎：在细胞分辨率下开发秀丽隐杆线虫大脑的生物扎根机械模型。在秀丽隐杆线虫神经生物学中实施具有适当接地的神经元和电路模型，例如基于生物学现实主义，将成功的模型应用于验证模型，以实现验证实验的预测，以实现未来的研究，并侧重于理解柔性，并理解柔韧性的变化。秀丽隐杆线虫的大脑，因此，其他动物的神经系统。该项目将设置为AI工具，使我们更接近这个目标。新颖的AI以及AI，模拟和复杂数据的集成将使其他数字双胞胎在生活和工程科学中受益。