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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Journal ArticleDOI
Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-based optimization to spiking neural networks
TL;DR: This article elucidates step-by-step the problems typically encountered when training SNNs and guides the reader through the key concepts of synaptic plasticity and data-driven learning in the spiking setting as well as introducing surrogate gradient methods, specifically, as a particularly flexible and efficient method to overcome the aforementioned challenges.
Journal ArticleDOI
Resistive switching materials for information processing
Zhongrui Wang,Huaqiang Wu,Geoffrey W. Burr,Cheol Seong Hwang,Kang L. Wang,Qiangfei Xia,Jianhua Yang +6 more
TL;DR: This Review surveys the four physical mechanisms that lead to resistive switching materials enable novel, in-memory information processing, which may resolve the von Neumann bottleneck and examines the device requirements for systems based on RSMs.
Journal ArticleDOI
Deep Learning With Spiking Neurons: Opportunities and Challenges.
Michael Pfeiffer,Thomas Pfeil +1 more
TL;DR: This review addresses the opportunities that deep spiking networks offer and investigates in detail the challenges associated with training SNNs in a way that makes them competitive with conventional deep learning, but simultaneously allows for efficient mapping to hardware.
Journal ArticleDOI
Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges.
Jianshi Tang,Fang Yuan,Xinke Shen,Zhongrui Wang,Mingyi Rao,Yuanyuan He,Yuhao Sun,Xinyi Li,Wenbin Zhang,Yijun Li,Bin Gao,He Qian,Guo-Qiang Bi,Sen Song,Jianhua Yang,Huaqiang Wu +15 more
TL;DR: A systematic overview of biological and artificial neural systems is given, along with their related critical mechanisms, and the existing challenges are highlighted to hopefully shed light on future research directions.
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EEG based multi-class seizure type classification using convolutional neural network and transfer learning
Shivarudhrappa Raghu,Shivarudhrappa Raghu,Natarajan Sriraam,Yasin Temel,Shyam Vasudeva Rao,Pieter L. Kubben +5 more
TL;DR: It can be concluded that the EEG based classification of seizure type using CNN model could be used in pre-surgical evaluation for treating patients with epilepsy.
References
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Proceedings Article
STDP enables spiking neurons to detect hidden causes of their inputs
TL;DR: It is shown here that STDP, in conjunction with a stochastic soft winner-take-all (WTA) circuit, induces spiking neurons to generate through their synaptic weights implicit internal models for subclasses (or "causes") of the high-dimensional spike patterns of hundreds of pre-synaptic neurons.
Journal ArticleDOI
Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke
Nikola Kasabov,Valery L. Feigin,Zeng-Guang Hou,Yixiong Chen,Linda Liang,Rita Krishnamurthi,Muhaini Othman,Priya Parmar +7 more
TL;DR: A novel method and system for personalised (individualised) modelling of spatio/spectro-temporal data (SSTD) and prediction of events and a novel evolving spiking neural network reservoir system (eSNNr) is proposed.
Journal ArticleDOI
First-Spike-Based Visual Categorization Using Reward-Modulated STDP
Milad Mozafari,Saeed Reza Kheradpisheh,Timothée Masquelier,Abbas Nowzari-Dalini,Mohammad Ganjtabesh +4 more
TL;DR: For the first time, it is shown that RL can be used efficiently to train a spiking neural network (SNN) to perform object recognition in natural images without using an external classifier.
Journal ArticleDOI
Fast and adaptive network of spiking neurons for multi-view visual pattern recognition
TL;DR: A new spiking neural network architecture and its corresponding learning procedure to perform fast and adaptive multi-view visual pattern recognition and the two main novelties of the network: structural adaptation and frame-by-frame accumulation of opinions are described.
Posted Content
Event-driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines
TL;DR: An event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.
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