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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Proceedings ArticleDOI
A computational model of the growth of dendritic spines with synaptic plasticity
TL;DR: In this work, a computational model of growth of dendritic spines due to synaptic plasticity is presented, built with spiking neural networks and synaptic dynamics.
Journal ArticleDOI
Differentiating signal from artefacts in cosmic ray detection: Applying Siamese spiking neural networks to CREDO experimental data
TL;DR: In this paper , a Siamese spiking neural network (SNN) model was proposed to tag artefacts appearing in the Cosmic Ray Extremely Distributed Observatory (CREDO) database.
Journal ArticleDOI
Discriminative training of spiking neural networks organised in columns for stream-based biometric authentication
TL;DR: In this article , a novel approach based on spiking neural networks (SNNs) is addressed for stream-based biometric authentication using an approach for inertial gait authentication.
Proceedings ArticleDOI
Knowledge Distillation between DNN and SNN for Intelligent Sensing Systems on Loihi Chip
Shiya Liu,Yang Yi +1 more
TL;DR: Zhang et al. as discussed by the authors proposed a DNN-SNN knowledge distillation algorithm to reduce the accuracy gap between DNNs and SNNs by transferring the knowledge between a deep neural network (DNN) and an SNN.
Journal ArticleDOI
Gender Determination in Human Voice Signals using Synaptic Efficacy Function-based Leaky Integrate and Fire Neuron Model
TL;DR: Günümüzdeki teknolojik gelişmeler, insanların bir sinyalinden konuşmacının cinsiyetini belirlemesi mümkün kılmıştır. as discussed by the authors
References
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Very Deep Convolutional Networks for Large-Scale Image Recognition
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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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