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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Proceedings ArticleDOI
Spiking Domain Feature Extraction with Temporal Dynamic Learning
Honghao Zheng,Yang Yi +1 more
TL;DR: In this article , a spiking domain feature extraction neural network with temporal multiplexing encoding is designed on EAGLE and fabricated on the PCB board, which can transfer the input information to multiplexed temporal encoded spikes and then utilize the spikes to adjust the synaptic weight voltage.
SNN-SC: A Spiking Semantic Communication Framework for Classification
TL;DR: Wang et al. as discussed by the authors proposed a spiking neural network (SNN) based digital semantic communication framework, namely SNN-SC, which is used to compress and extract the semantic information of classification model features in collaborative intelligence (CI) scenario.
Journal ArticleDOI
Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction
TL;DR: Wang et al. as discussed by the authors exploited the spatio-temporal feature extraction property of convolutional SNNs and proposed a new deep spiking architecture to tackle real-world classification and activity recognition tasks.
Long Short-term Memory with Two-Compartment Spiking Neuron
TL;DR: Liu et al. as discussed by the authors proposed a biologically inspired Long Short-Term Memory Leaky Integrate-and-Fire (LSTM-LIF) model, which incorporates carefully designed somatic and dendritic compartments that are tailored to retain short and long-term memories.
Book ChapterDOI
The Method of Structural Adaptation of the Compartmental Spiking Neuron Model
TL;DR: In this article , a method of structural training of a compartmental spike model of a neuron is proposed, where the training task was to form a response to a pattern represented by a vector of single spikes with time coding where each component of the vector entered a separate dendrite of the neuron.
References
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Very Deep Convolutional Networks for Large-Scale Image Recognition
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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