Spiking neural network with delay-adaption learning model for decision support in treating dental malocclusions

This paper presents a supervised delay-adaptaon Spiking Neural Network (SNN) model to support decision making in treang dental malocclusions, in parcular for orthodon c extracon. This temporal coding spiking neural network model employs a Hebbian-based rule to shi the connecon delays instead of previous approaches to delay selecon. Here, the tuned delays compensate for differences in the input firing mes of temporal paerns and enable them to coincide. The coincidence detec on capability of the spiking neuron has been ulised for detec ng and classifying paerns. The structure of the network is similar to that of an LVQ network except that the output layer neurons are coincidence-detecng spiking neurons. An input paern is represented by the group of the neuron that is the first to fire among all the compeng spiking neurons.