@inproceedings{9007af033ac2412d84d8367ae5bece3a,
title = "Feature extraction using spiking convolutional neural networks",
abstract = "Spiking neural networks are biologically plausible counterparts of the artificial neural networks. Conventional (non spiking) artificial neural networks are trained using a stochastic gradient descent algorithm (back propagation) while spiking neural networks are trained using the spike timing dependant plasticity algorithm. Training conventional neural networks is a memory and computationally/energy intensive job. Spiking networks show the potential to reduce both memory and energy usage. In this work, we consider spiking neural networks (SNN) as a classifier of the MNIST and NMNIST digits. We compare SNNs with convolutional neural networks in terms of classification accuracy, catastrophic forgetting.",
keywords = "Catastrophic forgetting, Datasets, Feature extraction, Neural networks, R-STDP, STDP",
author = "Ruthvik Vaila and John Chiasson and Vishal Saxena",
note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 2019 International Conference on Neuromorphic Systems, ICONS 2019 ; Conference date: 23-07-2019 Through 25-07-2019",
year = "2019",
month = jul,
day = "23",
doi = "10.1145/3354265.3354279",
language = "English",
series = "ACM International Conference Proceeding Series",
booktitle = "ICONS 2019 - Proceedings of International Conference on Neuromorphic Systems",
}