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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Journal ArticleDOI
EnforceSNN: Enabling resilient and energy-efficient spiking neural network inference considering approximate DRAMs for embedded systems
TL;DR: This work proposes EnforceSNN, a novel design framework that provides a solution for resilient and energy-efficient SNN inference using reduced-voltage DRAM for embedded systems and maintains the accuracy as compared to the baseline SNN with accurate DRAM.
Journal ArticleDOI
Damage detection and classification for sandwich composites using machine learning
TL;DR: In this paper , the authors leverage different machine learning algorithms for classifying the damages and non-damaged composites, and in which deep learning is significantly yield good accuracy, and it is developed for achieving high performance.
Proceedings ArticleDOI
DoB-SNN: A New Neuron Assembly-Inspired Spiking Neural Network for Pattern Classification
TL;DR: DoB-SNN as mentioned in this paper is inspired by a neuronal assembly where each neuron has a degree of belonging to every class of data being process and clusters the neurons during the training process using DoBs to allocate a group of neurons to each class.
Journal ArticleDOI
EDHA: Event-Driven High Accurate Simulator for Spike Neural Networks
Lingfei Mo,Xinao Chen,Gang Wang +2 more
TL;DR: An event-driven high accurate simulator named EDHA (Event-Driven High Accuracy) for spike neural networks is proposed in this paper, which reduces a large amount of calculations and achieves higher computational accuracy.
Journal ArticleDOI
Using multielectrode arrays to investigate neurodegenerative effects of the amyloid-beta peptide.
TL;DR: In this paper, the authors provide an overview on how multielectrode arrays are currently used in research on the amyloid-β peptide and its role in Alzheimer's disease, the most common neurodegenerative disorder.
References
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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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