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
Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition
TL;DR: This work uses SNNs for acoustic modeling and evaluates their performance on several large vocabulary recognition scenarios, demonstrating competitive ASR accuracies to their ANN counterparts while require only 10 algorithmic time steps and as low as 0.68 times total synaptic operations to classify each audio frame.
Journal ArticleDOI
Robust Spike-Based Continual Meta-Learning Improved by Restricted Minimum Error Entropy Criterion
TL;DR: A novel spike-based framework with minimum error entropy, called MeMEE, is proposed, using the entropy theory to establish the gradient-based online meta-learning scheme in a recurrent SNN architecture, which could be of great merit to applied developments with spike- based neuromorphic systems.
Journal ArticleDOI
Artificial Neuron using Vertical MoS 2 /Graphene Threshold Switching Memristors
Hirokjyoti Kalita,Adithi Krishnaprasad,Nitin Choudhary,Sonali Das,Durjoy Dev,Yi Ding,Laurene Tetard,Hee-Suk Chung,Yeonwoong Jung,Tania Roy +9 more
TL;DR: This work uses the volatile threshold switching behavior of a vertical-MoS2/graphene van der Waals heterojunction system to produce the integrate-and-fire response of a neuron, showing that the developed artificial neuron can play a crucial role in neuromorphic computing.
Journal ArticleDOI
Computing Primitive of Fully VCSEL-Based All-Optical Spiking Neural Network for Supervised Learning and Pattern Classification
TL;DR: The neuron-synapse self-consistent unified model of the all-optical SNN was developed, which enables reproducing the essential neuron-like dynamics and STDP function and paves the way toward fully VCSEL-based large-scale photonic neuromorphic systems with low power consumption.
Journal ArticleDOI
A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks
TL;DR: This work provides a foundation in spiking signal encoding and gives an overview over different application-oriented implementations which utilise the schemes.
References
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Proceedings ArticleDOI
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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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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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