@inproceedings{e0f3a5882cf34ce49a506f57118bc32f,
title = "NormAD - Normalized Approximate Descent based supervised learning rule for spiking neurons",
abstract = "NormAD is a novel supervised learning algorithm to train spiking neurons to produce a desired spike train in response to a given input. It is shown that NormAD provides faster convergence than state-of-the-art supervised learning algorithms for spiking neurons, often the gain in the rate of convergence being more than a factor of 10. The algorithm leverages the fact that a leaky integrate-and-fire neuron can be described as a non-linear spatio-temporal filter, allowing us to treat supervised learning as a mathematically tractable optimization problem with a cost function in terms of the membrane potential rather than the spike arrival time. A variant of stochastic gradient descent along with normalization has been used to derive the synaptic weight update rule. NormAD uses leaky integration of the input to determine the synaptic weight change. Since leaky integration is fundamental to all integrate-and-fire models of spiking neurons, we claim universal applicability of the learning rule to other models such as adaptive exponential integrate-and-fire model of neurons by demonstrating equally good performance in training with our algorithm.",
keywords = "LIF, NormAD, SNN, leaky integrate-and-fire, normalized approximate descent, spiking neuron, supervised learning",
author = "Navin Anwani and Bipin Rajendran",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; International Joint Conference on Neural Networks, IJCNN 2015 ; Conference date: 12-07-2015 Through 17-07-2015",
year = "2015",
month = sep,
day = "28",
doi = "10.1109/IJCNN.2015.7280618",
language = "English (US)",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2015 International Joint Conference on Neural Networks, IJCNN 2015",
address = "United States",
}