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 Content
EventProp: Backpropagation for Exact Gradients in Spiking Neural Networks
TL;DR: The backpropagation algorithm for spiking neural networks composed of leaky integrate-and-fire neurons operating in continuous time is derived, for the first time, by leveraging methods from optimal control theory to backpropagate errors through spike discontinuities without approximations or smoothing operations.
Posted ContentDOI
Neural spiking for causal inference
TL;DR: By introducing a local discontinuity with respect to their input drive, it is shown how spiking enables neurons to solve causal estimation and learning problems.
Journal ArticleDOI
Training much deeper spiking neural networks with a small number of time-steps
TL;DR: In this article , the authors proposed a novel error analysis framework that takes both the quantization error and deviation error into account, which comes from the discretization of SNN dynamicsthe neuron's coding scheme and the inconstant input currents at intermediate layers, respectively.
Journal ArticleDOI
Controller design for fixed-time synchronization of nonlinear coupled Cohen–Grossberg neural networks with switching parameters and time-varying delays based on synchronization dynamics analysis
TL;DR: This paper addresses the fixed-time synchronization controller design problem for a class of nonlinear coupled Cohen–Grossberg neural networks (NCCGNNs) with switching parameters and time-varying delays based on synchronization dynamics analysis.
Journal ArticleDOI
A multi-task learning framework for end-to-end aspect sentiment triplet extraction
TL;DR: Wang et al. as discussed by the authors decompose aspect sentiment triplet extraction into three subtasks, namely target tagging, opinion tagging, and sentiment tagging, which utilizes a series of target-specific tag sequences to identify the correspondences between opinion targets and opinion expressions and determine their sentiment polarities.
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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