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In the present study, we aim to understand neuronal controlling mechanisms by in- vestigating the locomotory neural circuit of the nematode Caenorhabditis elegans (C. elegans). C. elegans is a transparent 1mm roundworm which naturally inhabits in soil. Its stereotypic nervous system consists of only 302 identifiable neurons hard-wired through approximately 5000 chemical synapses and 2000 gap junctions. Because of highly con- centrated biological research on its neuronal network, C. elegans is one of the promising models to learn the controlling and learning principles applicable in development of brain-inspired artificial intelligence. We have identified crucial chemical and electrical synapses controlling the forward and backward tap withdrawal. Based on the acquired knowledge, we introduce a new fashion in designing of neuronal controllers by implementing simple stock market decision module. The decision module is composed of two sub-modules: 1. Indicator evaluation module compares the current and historical value of chosen stock market indicator, 2. C. elegans TW circuit mapping the forward and backward commands to BUY or SELL stocks.

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Balun, Ondrej
Ondrej finished his M.Sc. studies in software engineering at Vienna Technical University, Austria. He has obtained bachelor degree in applied computer science at Comenius University in Bratislava, Slovakia. Both of his theses - M.Sc. and Bc. - were dedicated to biologically motivated approaches in computer science.

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