Past and Recent Endeavours to Simulate Caenorhabditis elegans

Biological nervous systems are robust and highly adaptive information processing entities that excel current computer architectures in almost all aspects of sensory-motor integration. While they are slow and inefficient in the serial processing of stimuli or data chains, they outperform artificial computational systems in seemingly ordinary pattern recognition, orientation or navigation tasks. Even one of the simplest nervous systems in nature, that of the hermaphroditic nematode Caenorhabditis elegans with just 302 neurons and less than 8,000 synaptic connections, gives rise to a rich behavioural repertoire that – among controlling vital functions encodes different locomotion modalities (crawling, swimming and jumping). It becomes evident that both robotics and information and computation technology (ICT) would strongly benefit if the working principles of nervous systems could be extracted and applied to the engineering of brain-mimetic computational architectures. C. elegans, being one of the five best-characterized animal model systems, promises to serve as the most manageable organism to elucidate the information coding and control mechanisms that give rise to complex behaviour. This short paper reviews past and present endeavours to reveal and harvest the potential of nervous system function in C. elegans.