Neural network analysis of the information content in population responses from human periodontal receptors

• Understanding of the information processing in some sensory systems is hampered for several reasons. First, some of these systems may depend on several receptor types with different characteristics, and the crucial features of natural stimuli encoded by the receptors are rarely known with certainty. Second, the functional output of sensory processing is often not well defined. The human tooth is endowed with several types of sensory receptors. Among these, the mechanoreceptors located in the periodontal ligaments have been implicated in force encoding during chewing and biting. Individual receptors cannot, however, code unambiguously either the direction or the magnitude of the applied forces. Neuronal responses recorded in single human nerve fibers from periodontal receptors were fed to multi-layered feed-forward networks. The networks were trained with error back-propagation to identify specific features of the force stimuli that evoked the receptor responses. It was demonstrated that population responses in periodontal receptors contain information about both the point of attack and the direction of applied forces. It is concluded that networks may provide a powerful tool to investigate the information content in responses from biological receptor populations. As such, specific hypotheses with respect to information processing may be tested using neural networks also in sensory systems less well understood than, for instance, the visual system.

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