Deep learning in spiking neural networks
Amirhossein Tavanaei,Masoud Ghodrati,Saeed Reza Kheradpisheh,Timothée Masquelier,Anthony S. Maida +4 more
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TLDR
The emerging picture is that SNNs still lag behind ANNs in terms of accuracy, but the gap is decreasing, and can even vanish on some tasks, while SNN's typically require many fewer operations and are the better candidates to process spatio-temporal data.About:
This article is published in Neural Networks.The article was published on 2019-03-01 and is currently open access. It has received 756 citations till now. The article focuses on the topics: Spiking neural network & Artificial neural network.read more
Citations
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Journal ArticleDOI
Convolutional Spiking Neural Networks for Detecting Anticipatory Brain Potentials Using Electroencephalogram
TL;DR: The feasibility of using a convolutional spiking neural network (CSNN) as a classifier to detect anticipatory slow cortical potentials related to braking intention in human participants using an electroencephalogram (EEG) was studied.
Journal ArticleDOI
Developing Ecological Safety of Artificial Intelligence in Human Society
Aleksejs Zorins,Peteris Grabusts +1 more
TL;DR: In this paper, the authors presented cyber systems especially based on artificial intelligence (AI) from a perspective of ecological safety for humanity, and discussed its relevance to a modern science and society, as well as reviews risks of smart AI systems.
Journal ArticleDOI
Resolving Linguistic Ambiguities by Visual Context
Arianna Pavone,Alessio Plebe +1 more
Proceedings ArticleDOI
2D versus 3D Convolutional Spiking Neural Networks Trained with Unsupervised STDP for Human Action Recognition
TL;DR: In this article , a multi-layered 3D convolutional SNN model trained with unsupervised spike timing dependent plasticity (STDP) rule was proposed for human action recognition.
Proceedings ArticleDOI
Self-Supervised Spiking Neural Networks applied to Digit Classification
Benjamin Chamand,Philippe Joly +1 more
TL;DR: A self-supervised learning method applied to an SNN network is proposed, its feasibility is shown by training the architecture on a dataset of images of digits, then the representations are evaluated with two classification methods.
References
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Very Deep Convolutional Networks for Large-Scale Image Recognition
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TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
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