Maximizing survival when hungry: neural mechanisms for computing behavioural priorities

饥饿是一种强大的内在驱动力，可以显著改变动物的行为优先顺序。例如，饥饿的动物倾向于增加寻找食物的机会，但这伴随着捕食风险的增加。此外，作为一种节约能源的策略，非必要行为(如生殖)的表达受到下调。值得注意的是，饥饿还能在很大程度上改变动物对环境提示的反应方式；当喂食充足时，模棱两可的刺激可能被视为一种威胁，但随着饥饿的增加，这可能会被忽视，甚至被视为可能的食物提示。动物如何整合所有这些信息，并就从其全部行为中选择哪种动作达成共识，从而使其生存最大化？这些计算必须通过控制每一种不同行为的大脑回路之间的关键交互来解决。然而，要理解这些相互作用是具有挑战性的；它需要对每个回路有广泛的了解，并需要一种方法来同时监测整个大脑中的所有这些回路。在哺乳动物的神经系统中，这是不可能的，但更简单的动物必须使用不那么复杂的神经系统来解决完全相同的问题，这些神经系统很容易被审问。在这里，我们将使用一个非常广为人知的无脊椎动物系统，它的六种主要行为(摄食、运动、繁殖、撤回、呼吸、心脏控制)已经被广泛地描述到控制它们的单个识别神经元的水平。因此，当系统处理有关其内部饥饿状态和来自环境的线索的信息时，这提供了在线监测与生存相关的关键决策事件的机会。为了实现这一点，我们将利用行为和大脑记录方法的最新进展。具体地说，行为将使用新的机器学习算法来监控，这些算法可以跟踪动物的身体部位、姿势和行为单位(例如。馈送事件)。我们将使用实验室开发的一种新型荧光成像显微镜来分析大脑活动，以监测整个神经系统的神经元，直到单细胞水平。我们还将开发商业解决方案，允许在长时间内同时记录100-1000个神经元。我们将首先建立这种动物如何在大脑中所有产生行为的神经回路中编码关于其饥饿状态的信息，然后确定这些回路如何相互作用来决定选择哪种动作。随后，我们将研究神经回路是如何重新调整的，以便相同的输入可以在饥饿和进食时驱动完全不同的行为；这种输入感知意义的显著变化是一种高度适应性的机制，用于调整风险以适应动物的当前情况，但人们对此的神经基础知之甚少。使用真实世界的自然捕食者线索，我们还将测试动物在面临两个相互冲突的威胁时如何做出决定：捕食和饥饿。这将提供对控制动物最直接的与生存相关的决定的基本神经机制的洞察。随着动物开始面临气候变化和城市化导致的栖息地和食物供应的重大变化，这个话题的意义越来越大。这项建议直接与BBSRC的反应模式优先事项“3Rs”保持一致，通过使用非“受保护”的无脊椎动物物种“食物、营养和健康”，通过我们的深度学习行为跟踪方法和新颖的全中枢神经活动读出策略，确定参与代谢调节和“数据驱动生物学”的完整细胞和网络机制。这项工作的成果旨在提供对与生存相关的决策的基本理解，也与“生物科学的系统方法”有关。

Hunger is a potent internal drive that can significantly change an animal's behavioural priorities. For example, hungry animals favour actions that increase the chance of finding food, but this comes with an elevated risk of predation. Moreover, the expression of non-essential behaviours (e.g. reproduction) is down-regulated as an energy-conserving strategy. Remarkably, hunger can also substantially change the way an animal responds to environmental cues; when well-fed, an ambiguous stimulus might be perceived as a threat but with increased hunger this may be ignored or even considered as a possible food cue. How does an animal integrate all this information and reach a consensus decision about which action - from its full behavioural repertoire - to select, and thus maximize its survival?These computations must be solved by key interactions between the brain circuits that control each distinct behaviour. However, to understand these interactions is challenging; it requires an extensive knowledge of each circuit and a means to monitor all of them across the brain at the same time. In the mammalian nervous system, this is not possible but simpler animals must solve the exact same problems using less complex nervous systems that are highly-accessible for interrogation.Here, we will use a remarkably well-understood invertebrate system, Lymnaea, whose six principal behaviours (feeding, locomotion, reproduction, withdrawal, respiration, heart control) have been extensively characterized down to the level of the individual identified neurons that control them. As such, this provides the opportunity to monitor the key survival-linked decision-making events 'online' as the system processes information about both its internal hunger state and cues arising from the environment.To achieve this, we will exploit the latest advances in behaviour and brain recording approaches. Specifically, behaviours will be monitored using new machine-learning algorithms that can track animal body-parts, postures and units of behaviour (eg. feeding events) automatically. We will assay brain activity using a novel fluorescence imaging microscope developed in-lab to monitor neurons across the nervous system down to single cell level. We will also exploit commercial solutions that allow 100s-1000s of neurons to be recorded simultaneously over long-time periods.We will first establish how this animal encodes information about its hunger-state across all the behaviour-generating neural circuits in the brain and then determine how these circuits interact to decide which action to select. Subsequently, we will examine how neural circuits are re-tuned such that the same input can drive completely different behaviours when hungry versus when fed; this remarkable shift in the perceived meaning of an input is a highly-adaptive mechanism for adjusting risk to suit an animal's current situation, but the neural basis for it is poorly understood. Using real-world natural predator cues, we will also test how animals compute a decision when faced with two conflicting threats: predation versus starvation. This will provide insight into the fundamental neural mechanisms controlling an animal's most immediate survival-linked decisions.This topic has increasing significance as animals start to face major alterations to their habitat and food availability due to climate change and urbanization. This proposal aligns directly with the BBSRC responsive mode priorities '3Rs' by using a non-'protected' invertebrate species, 'Food, Nutrition and Health' through identifying integral cellular and network mechanisms involved in metabolic regulation and 'Data driven Biology' through our deep-learning behaviour-tracking approaches and novel whole-CNS neuronal activity readout strategies. The outputs from this work, which aim to provide a fundamental understanding of survival-linked decision-making, also have relevance to 'Systems Approaches to the Biosciences'.