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.
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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Journal ArticleDOI
Stdp-compatible approximation of backpropagation in an energy-based model
TL;DR: Simulations and a theoretical argument suggest that this rate-based update rule is consistent with those associated with spike-timing-dependent plasticity, and could be an element of a theory for explaining how brains perform credit assignment in deep hierarchies as efficiently as backpropagation does.
Proceedings ArticleDOI
Scalable energy-efficient, low-latency implementations of trained spiking Deep Belief Networks on SpiNNaker
Evangelos Stromatias,Daniel Neil,Francesco Galluppi,Michael Pfeiffer,Shih-Chii Liu,Steve Furber +5 more
TL;DR: A realization of a spike-based variation of previously trained DBNs on the biologically-inspired parallel SpiNNaker platform that achieves a classification performance of 95% on the MNIST handwritten digit dataset, which is only 0.06% less than that of a pure software implementation.
Proceedings ArticleDOI
Effective sensor fusion with event-based sensors and deep network architectures
Daniel Neil,Shih-Chii Liu +1 more
TL;DR: Several methods for preprocessing the spiking data from these sensors for use with various deep network architectures including a deep fusion network composed of Convolutional Neural Networks and Recurrent Neural Networks are discussed.
Journal ArticleDOI
Action Recognition Using a Bio-Inspired Feedforward Spiking Network
TL;DR: A bio-inspired feedforward spiking network modeling two brain areas dedicated to motion is proposed, and how the spiking output can be exploited in a computer vision application: action recognition is shown.
Journal ArticleDOI
A Novel Data-Driven Model for Real-Time Influenza Forecasting
Siva R. Venna,Amirhossein Tavanaei,Raju Gottumukkala,Vijay V. Raghavan,Anthony S. Maida,Stephen Nichols +5 more
TL;DR: This work proposes a novel data-driven machine learning method using long short-term memory (LSTM)-based multi-stage forecasting for influenza forecasting that performs better than the existing well-known influenza forecasting methods.
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