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
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A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration
TL;DR: SNN Calibration as mentioned in this paper proposes a calibration algorithm that can correct the error layer-by-layer by leveraging the knowledge within a pre-trained Artificial Neural Network (ANN).
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Integrated Photonic Neural Networks: Opportunities and Challenges
TL;DR: In this paper , the authors present a point of view and a suggestive roadmap in the field of integrated photonic platform for optical neural networks, highlighting recent progresses meeting with major challenges.
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SpikeBASE: Spiking Neural Learning Algorithm With Backward Adaptation of Synaptic Efflux
TL;DR: SpikeBASE as mentioned in this paper is a backpropagation-based SNN with backward adaptation of synapse efflux to coordinate the learning of synaptic strength, synaptic responses and multi-scale temporal memory formation.
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The Existence and Stability Analysis of Periodic Solution of Izhikevich Model
TL;DR: In this paper, a more realistic hybrid impulsive neuron model combining model with state-dependent impulsive effects is proposed by means of the theory of impulsive semidynamic system, the Poincare section and the ordinary differential equation geometry theory, the properties of the equilibrium points and the sufficient conditions for the existence and stability of different order 1 or order 2 periodic solutions of the system are derived near the equilibrium point or limit cycle.
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Direct learning-based deep spiking neural networks: a review
Yu-Zhu Guo,Xuhui Huang,Zhe Ma +2 more
TL;DR: A comprehensive survey of direct learning-based deep spiking neural networks is presented in this paper , mainly categorized into accuracy improvement methods, efficiency improvement methods and temporal dynamics utilization methods, and also divide these categorizations into finer granularities further to better organize and introduce them.
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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