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Spiking neural encoding models allow classification of real world tasks to suit for brain-machine interfaces in addition to serving as internal models. We developed a new spike encoding model inspired from cerebellum granular layer and tested different classification techniques like SVM, Naïve Bayes, MLP for training spiking neural networks to perform pattern recognition tasks on encoded datasets. As a precursor to spiking networkbased pattern recognition, in this study, real world datasetsdoi:10.1109/icacci.2015.7275845 dblp:conf/icacci/MediniZNVRD15 fatcat:7efwxa5mnvc5hpkhatpruzizd4