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This paper focuses on the study of a bio-inspired neural controller used to govern a mobile robot. The network's architecture is based on the understanding that neurophysiologists have obtained on the nervous system of some simple animals, like arthropods or invertebrates. The neuronal model mimics the behavior of the natural cells present in the animal, and elaborates the continuous signals coming from the robot's sensors. The output generated by the controller, after scaling, commands the wheel rotation and therefore the robot's linear and angular velocity. The mobile robot, thanks to the controller, presents different behaviors, like reaching a sonorous source, avoiding obstacles and finding the recharge stations. In the network architecture different modules, charged of different functionality, are regulated and coordinated using an inhibition mechanism. In order to test the control strategy and the neural architecture, we implemented the system in Matlab and finally in hardware using a dedicated dual processor board equipped with an ARM7TDMI micro-controller. Results show that the neural controller can govern the robot efficiently with performances comparable with those described about the animal.