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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Book ChapterDOI
Machine Learning Model Development Using Computational Neurology
TL;DR: In this paper , a machine learning model based on the spike response model (SRM-0) of neuronal dynamics was proposed for pattern recognition task of recognizing handwritten digits from the MNIST dataset.
Journal ArticleDOI
Signal Perceptron: On the Identifiability of Boolean Function Spaces and Beyond
TL;DR: This work shows that this re-formulated parallel mechanism can learn, with a single layer, any non-linear k-ary Boolean function and outperforms the single hidden layer multilayer perceptron in both Boolean function learning and image classification tasks, while also being faster and requiring fewer parameters.
Proceedings ArticleDOI
Comparative Analysis of Neural Networks and Deep Learning using Wireless Communication
G. K. Dixit,Viswanathasarma Ch,Valentino Joebert Barbosa,Syed Hamim Jeelani,Lalit Johari,Surendra Kumar Shukla +5 more
TL;DR: Deep Learning (DL) has outcompeted the Neural Network (NN) in terms of benefits in wireless communication and has the capacity to manage enormous amounts of data which is not possible using NN.
Posted Content
Multi-domain Collaborative Feature Representation for Robust Visual Object Tracking
TL;DR: In this article, a common feature extractor (CFE) was proposed to learn potential common representations from the RGB domain and event domain for boosting object tracking performance in challenge scenarios.
Journal ArticleDOI
Power-efficient gesture sensing for edge devices: mimicking fourier transforms with spiking neural networks
TL;DR: In this paper , the authors proposed an embedded gesture detection system that uses spiking neural networks (SNNs) applied directly to raw ADC data of a 6 0 G H z frequency modulated continuous wave radar.
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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