Deep learning in spiking neural networks
Amirhossein Tavanaei,Masoud Ghodrati,Saeed Reza Kheradpisheh,Timothée Masquelier,Anthony S. Maida +4 more
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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Posted ContentDOI
Toward One-Shot Learning in Neuroscience-Inspired Deep Spiking Neural Networks
TL;DR: A novel biological back-propagation based learning rule is developed and used to a train the network to detect basic features of different digits and information channels are constructed that are highly specific for each digit as matrix of synaptic connections between two layers of spiking neural networks.
Proceedings ArticleDOI
Exploring Spatiotemporal Functional Connectivity Dynamics of the Human Brain using Convolutional and Recursive Neural Networks
TL;DR: Initial findings show patterns of increasing artificial neural network accuracy linked to task evoked functional network coherence dynamics, which may improve feature detection and projection of learned features onto input space for translational applications.
Book ChapterDOI
Horizon Cyber-Vision: A Cybernetic Approach for a Cortical Visual Prosthesis
Mikel Val-Calvo,R. M. Ruiz,Leili Soo,Dorota Waclawczyk,Fabrizio Grani,José Manuel Ferrández,Eduardo Fernández +6 more
Proceedings ArticleDOI
Spiking Neural Networks with Single-Spike Temporal-Coded Neurons for Network Intrusion Detection
Shibo Zhou,Xiaohua Li +1 more
TL;DR: Wang et al. as mentioned in this paper analyzed the input-output expressions of both leaky and non-leaky neurons and showed that SNNs built with leaky neurons suffer from the overly-nonlinear and overly-complex input output response, which is the major reason for their difficult training and low performance.
Journal ArticleDOI
Hyperspectral Image Band Selection Based on CNN Embedded GA (CNNeGA)
TL;DR: In this paper , a deep network with 3D-convolutional layers embedded in a genetic algorithm (GA) was used as a fitness function to select the required number of spectral bands.
References
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Proceedings Article
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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