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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Journal ArticleDOI
Advancements in materials, devices, and integration schemes for a new generation of neuromorphic computers
Sina Najmaei,Andreu Glasmann,Marshall A. Schroeder,Wendy L. Sarney,Matthew L. Chin,Daniel M. Potrepka +5 more
TL;DR: Neuromorphic computing has emerged as the most promising successor to conventional complementary metal oxide semiconductor (CMOS) devices and von Neumann architecture as discussed by the authors , and the status of neuromorphic research, compares the traditional CMOS approach with neuromorphic devices for implementing biologically inspired circuits.
Journal ArticleDOI
Synaptic Activity and Hardware Footprint of Spiking Neural Networks in Digital Neuromorphic Systems
TL;DR: This study lead to the conclusion that spiking domain offers significant power and energy savings in sequential implementations and shows that synaptic activity is a critical factor that must be taken in account when addressing low-energy systems.
Journal ArticleDOI
A Scatter-and-Gather Spiking Convolutional Neural Network on a Reconfigurable Neuromorphic Hardware
TL;DR: Zhang et al. as discussed by the authors introduced a hardware-friendly conversion algorithm called ''scatter-and-gather'' to convert quantized ANNs to lossless SNNs, where neurons are connected with ternary -1,0,1 synaptic weights.
Journal ArticleDOI
Goal-driven, neurobiological-inspired convolutional neural network models of human spatial hearing
TL;DR: In this paper , a neurobiological-inspired convolutional neural network (CNN) model was proposed to predict human spatial hearing in naturalistic listening environments (e.g., with reverberation) using a mixture of spatial cues.
Journal ArticleDOI
Function Regression using Spiking DeepONet
TL;DR: This paper uses a DeepONet - neural network designed to learn operators - to learn the behavior of spikes, and uses this approach to do function regression, which has been a challenge due to the inherentulty in representing a function’s input domain and continuous output values as spikes.
References
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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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