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
Spiking Neural Network with Backpropagation Learning for Brain Visual Dynamics Decoding
Jake M Stauffer,Qingxue Zhang +1 more
TL;DR: In this article , a backpropagation learning algorithm was proposed to backward propagate the learning loss to each layer of the spiking network, thereby enabling automatic model updating, which can be propagated through both neuron somas and the synaptic connections, through specific gradient calculations.
Journal ArticleDOI
An Overview of Machine Learning Techniques in Local Path Planning for Autonomous Underwater Vehicles
TL;DR: In this paper , the authors present an overview of the state-of-the-art application of machine learning techniques on local path planning for AUVs, under supervised, unsupervised, and reinforcement learning.
Proceedings Article
A Critical Look into Cognitively-inspired Artificial Intelligence
TL;DR: In this paper , the authors highlight the main challenges and opportunities for cognitive inspiration for AI development and break down the source of inspiration into four abstraction levels in which the researcher may place an inspiration from.
Posted Content
Biologically Plausible Learning of Text Representation with Spiking Neural Networks
TL;DR: Empirical results demonstrate that the generated text representation may be effectively used in text classification leading to an accuracy of $80.19\%$ on the bydate version of the 20 newsgroups data set, which is a leading result amongst approaches that rely on low-dimensional text representations.
Proceedings ArticleDOI
Assembly-based STDP: A New Learning Rule for Spiking Neural Networks Inspired by Biological Assemblies
TL;DR: In this paper , a new variant of Spike-Timing Dependent Plasticity (STDP) is introduced, which is based on the assembly of neurons and expands the DoB-SNN's training algorithm for multilayer architectures.
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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