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In Caenorhabditis elegans, spatial orientation behavior in a chemical gradient (chemotaxis) involves bouts of turning (pirouettes) modulated by the change in concentration of attractant. Ablation of identified neurons has delineated a candidate neural network for chemotaxis in C. elegans. The aim of our research is to generate testable models of how the network computes behavioral state and consequently, turning frequency, in response to changes in concentration. We were able to train neural networks to exhibit known chemotaxis rules using experimental data from chemotaxing C. elegans. The resultant network solutions involved three to five dynamically contributing neurons. Here we have analyzed the three neuron solutions and found three distinguishing features: a fast excitatory and delayed inhibitory connection, which acts as a differentiator; self-connections, which act to regulate neural response speed similar to synaptic time-constants; and recurrent inhibitory connections, which regulate second order network response characteristics. We plan to use this model to predict and interpret the results of laser ablations of neurons and genetic mutation in the C. elegans chemotaxis network.