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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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
Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades
TL;DR: In this article, the authors proposed a method for converting existing Computer Vision static image datasets into Neuromorphic Vision datasets using an actuated pan-tilt camera platform, which eliminates timing artifacts introduced by monitor updates when simulating motion on a computer monitor.
Journal ArticleDOI
Reservoir Computing Trends
TL;DR: A brief introduction into basic concepts, methods, insights, current developments, and some applications of RC are given.
Proceedings ArticleDOI
A 45nm CMOS neuromorphic chip with a scalable architecture for learning in networks of spiking neurons
Jae-sun Seo,Bernard Brezzo,Yong Liu,Benjamin D. Parker,Steven K. Esser,Robert K. Montoye,Bipin Rajendran,Jose A. Tierno,Leland Chang,Dharmendra S. Modha,Daniel J. Friedman +10 more
TL;DR: A new architecture is proposed to overcome scalable learning algorithms for networks of spiking neurons in silicon by combining innovations in computation, memory, and communication to leverage robust digital neuron circuits and novel transposable SRAM arrays.
Journal ArticleDOI
2013 Special Issue: Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition
TL;DR: A new class of SNN, dynamic eSNN, that utilise both rank-order learning and dynamic synapses to learn SSTD in a fast, on-line mode and resulted in a superior performance in terms of accuracy and speed when compared with other SNN models that use either rank- order or STDP learning.
Journal ArticleDOI
Constraints on cortical and thalamic projections: the no-strong-loops hypothesis.
Francis Crick,Christof Koch +1 more
TL;DR: It is suggested that the connections between these cortical areas of the macaque monkey visual system never form strong, directed loops, and it is predicted that certain types of connections will not be found.
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