Oct 25, 2017 · In this paper, we present a method to learn event-based features in an unsupervised fashion called Spiking Convolutional Deep Belief Network (SCDBN).
Jan 16, 2024 · Spike-based communication between biological neurons is sparse and unreliable. This enables the brain to process visual information from the ...
[PDF] Spiking Convolutional Deep Belief Networks - ResearchGate
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Abstract. Understanding visual input as perceived by humans is a challenging task for machines. Today, most successful methods work by learning features ...
This paper presents the convolutional deep belief net- work, a hierarchical generative model that scales to full-sized images. Another key to our approach is ...
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This paper extends an unsupervised learning rule to train Spiking Restricted Boltzmann Machines by adding convolutions, lateral inhibitions and multiple ...
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Spiking convolutional deep belief networks. Published in In the proceedings of International Conference on Artificial Neural Networks (ICANN), 2017.
Jul 3, 2014 · Convolutional neural networks have performed better than DBNs by themselves in current literature on benchmark computer vision datasets such as MNIST.
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Feb 10, 2014 · "Spiking" refers to the activation of individual neurons, while "Deep" refers to the overall network architecture.
May 12, 2021 · The objective of this paper is to make the first step towards modern computer vision with supervised spiking neural networks. We propose a deep ...
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Spiking Convolutional Deep Belief Networks · Jacques Kaiser · David Zimmerer · J. Camilo Vasquez Tieck · Stefan Ulbrich · Arne Roennau · Rüdiger Dillmann ...