Investigations on the Nervous System of Caenorhabditis elegans

Introduction – Understanding the working principle of nervous systems of living species has long been a huge source of inspiration in the artificial intelligence (AI) community. In particular, the interpretation of reflexive behavior employing neural circuits. Reflex is considered to be the fundamental reason of many physiological behaviors in physical organs of creatures. High-level synchronization of the neural activities as well as having an understanding of particular physical action, is essential to represent such behavior in the brain. Providing an artificial platform that resembles the brain under which one is able to identify the corresponding working principles of such orchestrating neural activities is an extremely helpful step towards decoding the brainś perception of behavior. Accordingly, the nervous system of the nematode Caenorhabditis elegans (C. elegans) is a suitable system to be modeled due to its simplicity as it only has 302 identifiable neurons and a total of approximately 5,000 synapses. C. elegans has been in the focus of research for decades investigating its nervous system connectome [1], anatomy and physiology of individual neurons including gene expressions [2]. However, before having the ability of imaging the entire brain, studies on the distributed dynamics of the neural circuits in C. elegans has not been fully explored [3]. Moreover, a comprehensive machine learning approach to build up an artificial nervous system together with researching its dynamics is still an open topic to be investigated. In the present study, we create a platform for precisely looking at the nervous system of the C. elegans from a computer science point of view. We divide our research into four subgroups as follows: