Spiking neural networks for handwritten digit recognition—Supervised learning and network optimization
TLDR
The proposed SNN, which is trained based on the precise spike timing information outperforms an equivalent non-spiking artificial neural network (ANN) trained using back propagation, especially at low bit precision, shows the potential for realizing efficient neuromorphic systems that use spike based information encoding and learning for real-world applications.About:
This article is published in Neural Networks.The article was published on 2018-04-06 and is currently open access. It has received 116 citations till now. The article focuses on the topics: Spiking neural network & Artificial neural network.read more
Citations
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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
Automatic License Plate Recognition via sliding-window darknet-YOLO deep learning
Hendry,Hendry,Rung-Ching Chen +2 more
TL;DR: This work addresses the problem of car license plate detection using a You Only Look Once-darknet deep learning framework that uses YOLO's 7 convolutional layers to detect a single class.
Journal ArticleDOI
Opportunities for neuromorphic computing algorithms and applications
Catherine D. Schuman,Shruti R. Kulkarni,Maryam Parsa,J. Parker Mitchell,Prasanna Date,Bill Kay +5 more
TL;DR: A review of recent results in neuromorphic computing algorithms and applications can be found in this article , where the authors highlight characteristics of neuromorphic Computing technologies that make them attractive for the future of computing and discuss opportunities for future development of algorithms and application on these systems.
Journal ArticleDOI
Supervised learning in spiking neural networks: A review of algorithms and evaluations
TL;DR: This article presents a comprehensive review of supervised learning algorithms for spiking neural networks and evaluates them qualitatively and quantitatively, and provides five qualitative performance evaluation criteria and presents a new taxonomy for supervisedLearning algorithms depending on these five performance evaluated criteria.
Journal ArticleDOI
Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks
TL;DR: In this article, a deep convolutional spiking neural network (DCSNN) and a latency-coding scheme were used to address the limitations of deep artificial neural networks, which have revolutionized the computer vision domain.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
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Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
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A fast learning algorithm for deep belief nets
TL;DR: A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.
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Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
Geoffrey E. Hinton,Li Deng,Dong Yu,George E. Dahl,Abdelrahman Mohamed,Navdeep Jaitly,Andrew W. Senior,Vincent Vanhoucke,Patrick Nguyen,Tara N. Sainath,Brian Kingsbury +10 more
TL;DR: This article provides an overview of progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition.
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Receptive fields and functional architecture of monkey striate cortex
David H. Hubel,Torsten N. Wiesel +1 more
TL;DR: The striate cortex was studied in lightly anaesthetized macaque and spider monkeys by recording extracellularly from single units and stimulating the retinas with spots or patterns of light, with response properties very similar to those previously described in the cat.
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