A model that is consistent with several neurophysiological properties of biological head-direction cells is presented. The dynamics of the system is primarily controlled by idiothetic signals which determine the direction selectivity property. By means of LTP correlation learning, allothetic cues are incorporated to stabilize the direction representation over time. The interaction between allothetic and idiothetic signals to control head-direction cells is studied. Experimental results obtained by validating the model on a mobile Khepera robot are given. The neural system enables the robot to track its allocentric heading effectively.