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2018 | OriginalPaper | Chapter

A Neural Spiking Approach Compared to Deep Feedforward Networks on Stepwise Pixel Erasement

Authors : René Larisch, Michael Teichmann, Fred H. Hamker

Published in: Artificial Neural Networks and Machine Learning – ICANN 2018

Publisher: Springer International Publishing

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Abstract

In real world scenarios, objects are often partially occluded. This requires a robustness for object recognition against these perturbations. Convolutional networks have shown good performances in classification tasks. The learned convolutional filters seem similar to receptive fields of simple cells found in the primary visual cortex. Alternatively, spiking neural networks are more biological plausible. We developed a two layer spiking network, trained on natural scenes with a biologically plausible learning rule. It is compared to two deep convolutional neural networks using a classification task of stepwise pixel erasement on MNIST. In comparison to these networks the spiking approach achieves good accuracy and robustness.

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previous chapter Microsaccades for Neuromorphic Stereo Vision
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Metadata
Title
A Neural Spiking Approach Compared to Deep Feedforward Networks on Stepwise Pixel Erasement
Authors
René Larisch
Michael Teichmann
Fred H. Hamker
Copyright Year
2018
Book
Artificial Neural Networks and Machine Learning – ICANN 2018

Print ISBN: 978-3-030-01417-9

Electronic ISBN: 978-3-030-01418-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-01418-6_25

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