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arXiv · 1904.01908

Multi-layered Spiking Neural Network with Target Timestamp Threshold Adaptation and STDP

Pierre Falez·Pierre Tirilly·Ioan Marius Bilasco·Philippe Devienne·Pierre Boulet
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Abstract

Spiking neural networks (SNNs) are good candidates to produce ultra-energy-efficient hardware. However, the performance of these models is currently behind traditional methods. Introducing multi-layered SNNs is a promising way to reduce this gap. We propose in this paper a new threshold adaptation system which uses a timestamp objective at which neurons should fire. We show that our method leads to state-of-the-art classification rates on the MNIST dataset (98.60%) and the Faces/Motorbikes dataset (99.46%) with an unsupervised SNN followed by a linear SVM. We also investigate the sparsity level of the network by testing different inhibition policies and STDP rules.

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Pierre Falez, Pierre Tirilly, Ioan Marius Bilasco, Philippe Devienne, Pierre Boulet. 2019-04-03. Multi-layered Spiking Neural Network with Target Timestamp Threshold Adaptation and STDP. https://arxiv.org/abs/1904.01908

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