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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Journal ArticleDOI
Temporal precision in the neural code and the timescales of natural vision
Daniel A. Butts,Daniel A. Butts,Chong Weng,Jianzhong Jin,Chun-I Yeh,Chun-I Yeh,Nicholas A. Lesica,Jose-Manuel Alonso,Garrett B. Stanley +8 more
TL;DR: It is demonstrated that the relevant timescale of neuronal spike trains depends on the frequency content of the visual stimulus, and that ‘relative’, not absolute, precision is maintained both during spatially uniform white-noise visual stimuli and naturalistic movies.
Journal ArticleDOI
Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks.
TL;DR: A spatio-temporal backpropagation (STBP) algorithm for training high-performance SNNs is proposed, which combines the layer-by-layer spatial domain (SD) and the timing-dependent temporal domain (TD), and does not require any additional complicated skill.
Journal ArticleDOI
A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection
TL;DR: A new Multi-Spiking Neural Network (MuSpiNN) model is presented in which information from one neuron is transmitted to the next in the form of multiple spikes via multiple synapses and the model and learning algorithm employ the heuristic rules and optimum parameter values presented by the authors in a recent paper that improved the efficiency of the original single-spiking SNN model by two orders of magnitude.
Journal ArticleDOI
HOTS: A Hierarchy of Event-Based Time-Surfaces for Pattern Recognition
TL;DR: The central concept is to use the rich temporal information provided by events to create contexts in the form of time-surfaces which represent the recent temporal activity within a local spatial neighborhood and it is demonstrated that this concept can robustly be used at all stages of an event-based hierarchical model.
Posted Content
Metric-space analysis of spike trains: theory, algorithms, and application
TL;DR: The mathematical basis of a new approach to the analysis of temporal coding is the construction of several families of novel distances (metrics) between neuronal impulse trains that formalize physiologically based hypotheses for those aspects of the firing pattern that might be stimulus dependent and make essential use of the point-process nature of neural discharges.
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