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 memory-motor models. In this system, a single 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 artifical 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 5-V level. The system has been constructed and successfully used to demonstrate empirical physiologically based models of inhibitory positional feedback and synaptic learning.