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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Symbiosis of an artificial neural network and models of biological neurons: training and testing
TL;DR: In this article , the authors used the FitzHugh-Nagumo (FHN) system as an example of model demonstrating simplified neuron activity to create and identify the features of an artificial neural network (ANN).
Proceedings Article
Exact Gradient Computation for Spiking Neural Networks via Forward Propagation
TL;DR: In this paper , the implicit function theorem is applied to SNNs at the discrete spike times, and a novel training algorithm, called forward propagation (FP), is proposed to compute exact gradients for SNN.
Journal ArticleDOI
Accelerating SNN Training with Stochastic Parallelizable Spiking Neurons
TL;DR: In this article , the Stochastic Parallelizable Spiking Neuron (SPSN) is proposed to overcome the sequential training limitation of LIF neurons by separating the linear integration component from the non-linear spiking function.
Journal ArticleDOI
Neural networks implementation for the environmental optimisation of the recycled concrete aggregate inclusion in warm mix asphalt
Rodrigo Polo-Mendoza,Gilberto Martinez-Arguelles,Rita Peñabaena-Niebles,Elvis Covilla-Valera +3 more
TL;DR: In this article , the authors developed a computational model to optimize the WMA-RCA design, which was based on the postulates of the statistical learning theory, i.e., preferring to generate learning through low-complexity models.
Ion-dynamic Capacitance Enables Multimode Transistors and Multimode Neural Networks
Xiaoci Liang,Yiyang Luo +1 more
TL;DR: In this paper , the authors derive a concise model to describe the complex transient ion-dynamic capacitance in transistors and reveal that the aforementioned characteristics could all be achieved at different operational modes on demand in a single transistor.
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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