Spiking Convolutional Deep Belief Networks

Publikationstyp
Konferenz
Autor(en)
Kaiser, Jacques and Zimmerer, David and Tieck, J. Camilo Vasquez and Ulbrich, Stefan and Roennau, Arne and Dillmann, Rüdiger
Jahr
2017
Seiten
3--11
Verlag
Springer International Publishing
Adresse
Cham
ISBN
978-3-319-68612-7
Buchtitel
Artificial Neural Networks and Machine Learning -- ICANN 2017
Bearbeiter
Lintas, Alessandra and Rovetta, Stefano and Verschure, Paul F.M.J. and Villa, Alessandro E.P.
Abstract
Understanding visual input as perceived by humans is a challenging task for machines. Today, most successful methods work by learning features from static images. Based on classical artificial neural networks, those methods are not adapted to process event streams as provided by the Dynamic Vision Sensor (DVS). Recently, an unsupervised learning rule to train Spiking Restricted Boltzmann Machines has been presented [9]. Relying on synaptic plasticity, it can learn features directly from event streams. In this paper, we extend this method by adding convolutions, lateral inhibitions and multiple layers. We evaluate our method on a self-recorded DVS dataset as well as the Poker-DVS dataset. Our results show that our convolutional method performs better and needs less parameters. It also achieves comparable results to previous event-based classification methods while learning features in an unsupervised fashion.
DOI
10.1007/978-3-319-68612-7_1
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Eingetragen von
Arne Rönnau