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

Author

Philippe Devienne, Pierre Tirilly, Pierre Boulet, Pierre Falez, Ioan Marius Bilasco, Falez, Pierre, Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Ecole nationale supérieure Mines-Télécom Lille Douai (IMT Lille Douai), and Institut Mines-Télécom [Paris] (IMT)

Subjects
FOS: Computer and information sciences, Computer science, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, [INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Convolutional neural network, 03 medical and health sciences, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, Neural and Evolutionary Computing (cs.NE), Adaptation (computer science), Spiking neural network, business.industry, Computer Science - Neural and Evolutionary Computing, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Pattern recognition, Pattern recognition (psychology), Unsupervised learning, 020201 artificial intelligence & image processing, Timestamp, Artificial intelligence, business, 030217 neurology & neurosurgery, MNIST database
Abstract

International audience; 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.

Published
2019
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