In order to investigate neuronally-derived algorithms for feedback and control, one joint of a conventional robotic arm was fitted with sensory and motor devices consistent with biological sensory-motor models. In this system, a simple potentiometer senses angular position of a given joint. From this signal, the forward and reverse velocity are derived, as are the acceleration and deceleration. Controlling the stepper motor that drives this joint is an IC-based artificial neuron that has been specifically designed for this task. It possesses multiple excitatory and inhibitory input ports, and its output signals are amplified to a full five-volt level. The output pulse train from the motor nerve circuit is also used to clock an on-chip four-bit bidirectional shift register that excites the four phases of the stepper motor in the proper forward or reverse sequence. The system has been constructed and successfully used to demonstrate empirical physiologically-based models of inhibitory positional feedback and synaptic learning. Easily adapted to other neuronal circuits, this system will serve as an excellent platform for the design and verification of biological models of motion, as applied to mechanical limbs.