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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Partial success in closing the gap between human and machine vision
Robert Geirhos,Kantharaju Narayanappa,Benjamin Mitzkus,Tizian Thieringer,Matthias Bethge,Felix A. Wichmann,Wieland Brendel +6 more
TL;DR: This article investigated a range of promising machine learning developments that crucially deviate from standard supervised CNNs along three axes: objective function (self-supervised, adversarially trained, CLIP language-image training), architecture (e.g. vision transformers), and dataset size (ranging from 1M to 1B).
Proceedings ArticleDOI
Faster and Simpler SNN Simulation with Work Queues
TL;DR: A clock-driven Spiking Neural Network simulator which is up to 3x faster than the state of the art while, at the same time, being more general and requiring less programming effort on both the user and maintainer’s side is presented.
Journal ArticleDOI
Heterogeneous recurrent spiking neural network for spatio-temporal classification
TL;DR: In this article , a heterogeneous recurrent spiking neural network (HRSNN) with unsupervised learning for spatio-temporal classification of video activity recognition tasks was proposed.
Proceedings ArticleDOI
Early Image Termination Technique During STDP Training of Spiking Neural Network
Dongwoo Lew,Jongsun Park +1 more
TL;DR: In this paper, a Spike Time Dependent Plasticity (STDP) is used to reduce redundant time steps during training since STDP cannot determine current image needs further training or not.
Posted ContentDOI
A Brief Review on Spiking Neural Network - A Biological Inspiration
TL;DR: A brief introduction to SNN is presented, which incorporates the mathematical structure, applications, and implementation of SNN, which connects neuroscience and machine learning to establish high-level efficient computing.
References
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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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