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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.