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
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.
Journal ArticleDOI
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 ArticleDOI
Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
TL;DR: In this paper, a set of optimization techniques to minimize performance loss in the conversion process for convolutional networks and fully connected deep networks are presented, which yield networks that outperform all previous SNNs on the MNIST database.
Journal ArticleDOI
Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification.
Bodo Rueckauer,Iulia-Alexandra Lungu,Yuhuang Hu,Michael Pfeiffer,Michael Pfeiffer,Shih-Chii Liu +5 more
TL;DR: This paper shows conversion of popular CNN architectures, including VGG-16 and Inception-v3, into SNNs that produce the best results reported to date on MNIST, CIFAR-10 and the challenging ImageNet dataset.
Journal ArticleDOI
Convolutional networks for fast, energy-efficient neuromorphic computing
Steven K. Esser,Paul A. Merolla,John V. Arthur,Andrew S. Cassidy,Rathinakumar Appuswamy,Alexander Andreopoulos,David Berg,Jeffrey L. McKinstry,Timothy Melano,R Davis,Carmelo di Nolfo,Pallab Datta,Arnon Amir,Brian Taba,Myron D. Flickner,Dharmendra S. Modha +15 more
TL;DR: This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.
Journal ArticleDOI
Rapid Neural Coding in the Retina with Relative Spike Latencies
Tim Gollisch,Markus Meister +1 more
TL;DR: It is reported that certain retinal ganglion cells encode the spatial structure of a briefly presented image in the relative timing of their first spikes, which allows the retina to rapidly and reliably transmit new spatial information with the very first spikes emitted by a neural population.
Posted Content
Deep Learning in Bioinformatics
TL;DR: This review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies and suggest future research directions.
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