1 research outputs found
A Neural Spiking Approach Compared to Deep Feedforward Networks on Stepwise Pixel Erasement
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.Comment: Published in ICANN 2018: Artificial Neural Networks and Machine
Learning - ICANN 2018
https://link.springer.com/chapter/10.1007/978-3-030-01418-6_25 The final
authenticated publication is available online at
https://doi.org/10.1007/978-3-030-01418-6_2