Spike encoding for pattern recognition: Comparing cerebellum granular layer encoding and BSA algorithms
| Autor: | Bipin G. Nair, Asha Vijayan, Shyam Diwakar, Chaitanya Medini, Ritu Maria Zacharia, Lekshmi Priya Rajagopal |
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| Rok vydání: | 2015 |
| Předmět: |
Spiking neural network
Cerebellum Quantitative Biology::Neurons and Cognition Computer science business.industry Pattern recognition Granular layer Support vector machine Naive Bayes classifier ComputingMethodologies_PATTERNRECOGNITION medicine.anatomical_structure Encoding (memory) Pattern recognition (psychology) medicine Spike (software development) Neuron Artificial intelligence business Algorithm |
| Zdroj: | ICACCI |
| DOI: | 10.1109/icacci.2015.7275845 |
| Popis: | 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, Naive Bayes, MLP for training spiking neural networks to perform pattern recognition tasks on encoded datasets. As a precursor to spiking network-based pattern recognition, in this study, real world datasets were 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. |
| Databáze: | OpenAIRE |
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