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
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An ensemble-based approach for automated medical diagnosis of malaria using EfficientNet
TL;DR: In this paper , a support decision system for detecting malaria from microscopic peripheral blood cells images through convolutional neural networks (CNN) is proposed, which is based on EfficientNetB0 architecture.
Proceedings ArticleDOI
Minibatch Processing for Speed-up and Scalability of Spiking Neural Network Simulation
TL;DR: This work provides an implementation of mini-batch processing applied to clock-based SNN simulation, leading to drastically increased data throughput and different parameter reduction techniques are shown to produce different learning outcomes in a simulation of networks trained with spike-timing-dependent plasticity.
Journal ArticleDOI
Encoding primitives generation policy learning for robotic arm to overcome catastrophic forgetting in sequential multi-tasks learning
TL;DR: This paper proposes to overcome catastrophic forgetting with the focus on learning a series of robotic tasks sequentially, and develops a novel hierarchical neural network's framework called Encoding Primitives Generation Policy Learning (E-PGPL) to enable continual learning with two components.
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Modeling bottom-up and top-down attention with a neurodynamic model of V1
David Berga,Xavier Otazu +1 more
TL;DR: Results show that the model outpeforms other biologically inspired models of saliency prediction while predicting visual saccade sequences with the same model, as well as how distinct search strategies can predict attention at distinct image contexts.
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Goal-driven, neurobiological-inspired convolutional neural network models of human spatial hearing
TL;DR: The results show that neurobiological-inspired CNN models trained on real-life sounds spatialized with human binaural hearing characteristics can accurately predict sound location in the horizontal plane and reveal a gradient of spatial selectivity across network layers, which paves the way for future studies combining neural network models with empirical measurements of neural activity.
References
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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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