Spike encoding for pattern recognition: Comparing cerebellum granular layer encoding and BSA algorithms

Chaitanya Medini, Ritu Maria Zacharia, Bipin Nair, Asha Vijayan, Lekshmi Priya Rajagopal, Shyam Diwakar
2015 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)  
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 datasets
more » ... encoded into spike trains. The objective of this study was to encode information from datasets into spiking neuron patterns that were relevant for spiking neural networks and for conventional machine learning algorithms. In this initial study, we present a new approach similar to cerebellum granular layer encoding and compared it with BSA encoding techniques. We have also compared the efficiency of the encoded dataset with different datasets and with standard machine learning algorithms.
doi:10.1109/icacci.2015.7275845 dblp:conf/icacci/MediniZNVRD15 fatcat:7efwxa5mnvc5hpkhatpruzizd4