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
Towards Low-Power Machine Learning Architectures Inspired by Brain Neuromodulatory Signalling
Taylor Barton,Hao Yu,Kyle Rogers,Nancy Fulda,Shiuh-hua Wood Chiang,Jordan T. Yorgason,Karl F. Warnick +6 more
TL;DR: In this paper , a transfer learning method inspired by modulatory neurotransmitter mechanisms in biological brains and explore applications for neuromorphic hardware is presented. But the method is limited to the domain of image recognition in both feed forward deep learning and spiking neural network architectures.
Proceedings ArticleDOI
Neuromorphic technologies for defence and security
TL;DR: This paper reports on the current state of the art in the field of NM systems, and it describes three application scenarios of SNN-based processing for security and defence, namely target detection and tracking, semantic segmentation, and control.
Journal ArticleDOI
Temporal State Machines: Using temporal memory to stitch time-based graph computations
TL;DR: This work proposes to associate race logic with the mathematical field of tropical algebra, enabling a more methodical approach toward building temporal circuits, and leverages analog memristor-based temporal memories to design such a state machine that operates purely on time-coded wavefronts.
Journal ArticleDOI
Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST
TL;DR: It is concluded that GeNN is in advantage for large networks and real-time applications while NEST plays out its strengths in a high degree of flexibility ideal for prototyping, and easy access for non-expert programmers.
Journal ArticleDOI
SEENN: Towards Temporal Spiking Early-Exit Neural Networks
TL;DR: In this paper , a fine-grained adjustment of the number of timesteps in SNNs is proposed to reduce redundant timestep for certain data, which is called Spiking Early-Exit Neural Networks (SEENNs).
References
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Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
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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