A C. elegans whole-brain digital twin

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.