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Deep learning in spiking neural networks

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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.
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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.

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Citations
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Journal ArticleDOI

Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks

TL;DR: In this article, a deep convolutional spiking neural network (DCSNN) and a latency-coding scheme were used to address the limitations of deep artificial neural networks, which have revolutionized the computer vision domain.
Journal ArticleDOI

Temporal Backpropagation for Spiking Neural Networks with One Spike per Neuron.

TL;DR: S4NN as discussed by the authors proposes a rank-order-coding-based learning rule for multilayer spiking neural networks (SNNs) that use a form of temporal coding known as rankorder coding, where neurons fire exactly one spike per stimulus, but the firing order carries information.
Journal ArticleDOI

The building blocks of a brain-inspired computer

TL;DR: This review points to the important primitives of a brain-inspired computer that could drive another decade-long wave of computer engineering.
Journal ArticleDOI

Polymer Analog Memristive Synapse with Atomic-Scale Conductive Filament for Flexible Neuromorphic Computing System.

TL;DR: It is demonstrated that the transition of the operation mode in poly(1, 3,5-trivinyl-1,3,5 -trimethyl cyclotrisiloxane) (pV3D3)-based flexible memristor from conventional binary to synaptic analog switching can be achieved simply by reducing the size of the formed filament.
Journal ArticleDOI

Neuromorphic Engineering: From Biological to Spike‐Based Hardware Nervous Systems

TL;DR: Fundamental knowledge related to the structures and working principles of neurons and synapses of the biological nervous system is reviewed and an overview is provided on the development of neuromorphic hardware systems, from artificial synapses and neurons to spike‐based neuromorphic computing platforms.
References
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Proceedings ArticleDOI

Deep Residual Learning for Image Recognition

TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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ImageNet Classification with Deep Convolutional Neural Networks

TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI

Long short-term memory

TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
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Very Deep Convolutional Networks for Large-Scale Image Recognition

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

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.
Related Papers (5)
Trending Questions (1)
What is the relationship between spiking neural networks and neuromorphics?

The paper mentions that spiking neural networks (SNNs) are more biologically realistic than artificial neural networks (ANNs) and are the better candidates to process spatio-temporal data. Additionally, SNNs combined with bio-plausible local learning rules make it easier to build low-power, neuromorphic hardware. Therefore, the relationship between SNNs and neuromorphics is that SNNs are a suitable approach for implementing neuromorphic hardware.