Maximizing survival when hungry: neural mechanisms for computing behavioural priorities

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'.