WHole Animal Modelling (WHAM): Toward the integrated understanding of sensory motor control in C. elegans

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Animals are remarkable creatures. No man-made machine even comes close in its ability to navigate complex environments, respond to rich sensory cues, learn and adapt its behaviour when encountering completely novel scenarios and much much more. But even the simplest animals, ruled by even the simplest nervous systems, can achieve this. Simple animals may not be able to play chess or balance bank statements, but there is much we can learn from them about their robust mechanisms for sensory-integration and motor control which may be of use to us in control engineering, bio-robotics and even in future brain-machine interfaces that are being developed for neuro-prosthetic applications.An excellent starting point for understanding animal behaviour is a tiny, free living 1mm long roundworm, called C. elegans. By comparison to our 100 billion nerve cells, or even a fly's 100 thousand nerve cells, this worm's entire nervous system consists of a mere 302 nerve cells. Unlike our nervous system, the worm's circuitry is hard wired and identical across individuals of the species, making it possible to study rigorously and reproducibly. Due in part to its simplicity, and in part to its ease of manipulation in the lab, this is the only animal for which this entire nervous system has been mapped in exquisite detail (to sub-cellular resolution). But despite its relative simplicity, this worm possesses many of the functions that are attributed to more complex animals, including feeding, mating, complex sensory abilities, memory and learning. It is not surprising, therefore, that the modelling of this worm has captured the imagination of physicists, computer scientists and engineers alike. The integrated modelling of C. elegans has even been proposed as one of the UK's ``Grand Challenges for Computing Research.''In this Fellowship, I will begin to integrate our understanding of C. elegans sensory motor behaviour in a single computational model. The challenge is to bridge the gap between the effectively static neural circuit architecture and the dynamic neural computation it sustains. This fellowship will enable me to deliver a step change, not only in our understanding of an important model organism, but also in advancing the science and engineering of complex systems, whether in the context of reverse engineering real world networks, or in the context of designing them.