Deep learning in spiking neural networks
Amirhossein Tavanaei,Masoud Ghodrati,Saeed Reza Kheradpisheh,Timothée Masquelier,Anthony S. Maida +4 more
Reads0
Chats0
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
More filters
Journal ArticleDOI
Evolution of Bio-Inspired Artificial Synapses: Materials, Structures, and Mechanisms.
TL;DR: This work reviews recent progress on artificial synapses, and synaptic plasticity and functional emulation are introduced, and then synaptic electronic devices for neuromorphic computing systems are discussed.
Journal ArticleDOI
SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks With at Most One Spike per Neuron.
TL;DR: SpykeTorch as discussed by the authors is an open-source high-speed simulation framework based on PyTorch, which simulates convolutional SNNs with at most one spike per neuron and the rank-order encoding scheme.
Journal ArticleDOI
Efficient training and design of photonic neural network through neuroevolution
TL;DR: In this paper, two typical neuroevolution algorithms are used to determine the hyper-parameters of ONNs and optimize the weights (phase shifters) in the connections.
Journal ArticleDOI
Experimental Demonstration of Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses.
S. R. Nandakumar,S. R. Nandakumar,Irem Boybat,Irem Boybat,Manuel Le Gallo,Evangelos Eleftheriou,Abu Sebastian,Bipin Rajendran +7 more
TL;DR: In this paper, the authors evaluate the feasibility to realize high-performance event-driven in-situ supervised learning systems using nanoscale and stochastic analog memory synapses.
Proceedings ArticleDOI
Event-Driven Visual-Tactile Sensing and Learning for Robots
Tasbolat Taunyazov,Weicong Sng,Brian Lim,Hian-Hian See,Jethro Kuan,Abdul Fatir Ansari,Benjamin C. K. Tee,Harold Soh +7 more
TL;DR: This work contributes an event-driven visual-tactile perception system, comprising a novel biologically-inspired tactile sensor and multi-modal spike-based learning, and the Visual-Tactile Spiking Neural Network (VT-SNN), which enables fast perception when coupled with event sensors.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Related Papers (5)
Loihi: A Neuromorphic Manycore Processor with On-Chip Learning
Michael Davies,Narayan Srinivasa,Tsung-Han Lin,Gautham N. Chinya,Cao Yongqiang,Sri Harsha Choday,Georgios D. Dimou,Prasad Joshi,Nabil Imam,Shweta Jain,Yuyun Liao,Chit-Kwan Lin,Andrew Lines,Ruokun Liu,Deepak A. Mathaikutty,Steven McCoy,Arnab Paul,Jonathan Tse,Guruguhanathan Venkataramanan,Yi-Hsin Weng,Andreas Wild,Yoon Seok Yang,Hong Wang +22 more
Training Deep Spiking Neural Networks Using Backpropagation.
A million spiking-neuron integrated circuit with a scalable communication network and interface
Paul A. Merolla,John V. Arthur,Rodrigo Alvarez-Icaza,Andrew S. Cassidy,Jun Sawada,Filipp Akopyan,Bryan L. Jackson,Nabil Imam,Chen Guo,Yutaka Nakamura,Bernard Brezzo,Ivan Vo,Steven K. Esser,Rathinakumar Appuswamy,Brian Taba,Arnon Amir,Myron D. Flickner,William P. Risk,Rajit Manohar,Dharmendra S. Modha +19 more