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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Book ChapterDOI
Decoding nociception in the spinal cord: Computer modeling and machine learning
Adriel Barrios-Anderson,Adriel Barrios-Anderson,Jared Fridley,Jared Fridley,David A. Borton,David A. Borton,Carl Y Saab +6 more
TL;DR: In this article, the authors highlight the advances that have been made using ML to allow computer-based algorithms to predict SCI clinical outcomes, analyze imaging data to accurately diagnose and identify neural injury, and predict whether individuals have chronic pain based on brain imaging or EEG patterns.
Journal ArticleDOI
Batch normalization-free weight-binarized SNN based on hardware-saving IF neuron
TL;DR: Zhang et al. as discussed by the authors proposed a batch normalization (BN)-free weight binarized SNN based on hardware-saving IF neurons to reduce storage requirements and improve the computational efficiency of neuromorphic computing.
Journal ArticleDOI
Feed-Forward Optimization With Delayed Feedback for Neural Networks
TL;DR: Feed-Forward with delayed feedback (F$^3$) as discussed by the authors improves upon prior work by utilizing delayed error information as a sample-wise scaling factor to approximate gradients more accurately.
Journal ArticleDOI
Spike-Event Object Detection for Neuromorphic Vision
TL;DR: Zhang et al. as discussed by the authors proposed a solution to the novel object detection method with spike events, where spike events are first encoded to event images according to the computational methodology of neuromorphic theory and then realized as change-detected images of moving objects with a high frame rate.
Journal ArticleDOI
Spiking sampling network for image sparse representation and dynamic vision sensor data compression
Chunming Jiang,Yilei Zhang +1 more
TL;DR: Wang et al. as mentioned in this paper proposed a spiking sampling network composed of spiking neurons, which can dynamically decide which pixel points should be retained and which ones need to be masked according to the input.
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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