Toward Scalable, Efficient, and Accurate Deep Spiking Neural Networks With Backward Residual Connections, Stochastic Softmax, and Hybridization
出版年份 2020 全文链接
标题
Toward Scalable, Efficient, and Accurate Deep Spiking Neural Networks With Backward Residual Connections, Stochastic Softmax, and Hybridization
作者
关键词
-
出版物
Frontiers in Neuroscience
Volume 14, Issue -, Pages -
出版商
Frontiers Media SA
发表日期
2020-06-30
DOI
10.3389/fnins.2020.00653
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- ReStoCNet: Residual Stochastic Binary Convolutional Spiking Neural Network for Memory-Efficient Neuromorphic Computing
- (2019) Gopalakrishnan Srinivasan et al. Frontiers in Neuroscience
- Going Deeper in Spiking Neural Networks: VGG and Residual Architectures
- (2019) Abhronil Sengupta et al. Frontiers in Neuroscience
- Towards artificial general intelligence with hybrid Tianjic chip architecture
- (2019) Jing Pei et al. NATURE
- Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-Based Optimization to Spiking Neural Networks
- (2019) Emre O. Neftci et al. IEEE SIGNAL PROCESSING MAGAZINE
- Towards spike-based machine intelligence with neuromorphic computing
- (2019) Kaushik Roy et al. NATURE
- STDP-based spiking deep convolutional neural networks for object recognition
- (2018) Saeed Reza Kheradpisheh et al. NEURAL NETWORKS
- ASP: Learning to Forget With Adaptive Synaptic Plasticity in Spiking Neural Networks
- (2018) Priyadarshini Panda et al. IEEE Journal on Emerging and Selected Topics in Circuits and Systems
- Deep Spiking Convolutional Neural Network Trained with Unsupervised Spike Timing Dependent Plasticity
- (2018) Chankyu Lee et al. IEEE Transactions on Cognitive and Developmental Systems
- Training Deep Spiking Convolutional Neural Networks With STDP-Based Unsupervised Pre-training Followed by Supervised Fine-Tuning
- (2018) Chankyu Lee et al. Frontiers in Neuroscience
- STDP-based Unsupervised Feature Learning using Convolution-over-time in Spiking Neural Networks for Energy-Efficient Neuromorphic Computing
- (2018) Gopalakrishnan Srinivasan et al. ACM Journal on Emerging Technologies in Computing Systems
- Deep Learning With Spiking Neurons: Opportunities and Challenges
- (2018) Michael Pfeiffer et al. Frontiers in Neuroscience
- A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing
- (2017) Yoeri van de Burgt et al. NATURE MATERIALS
- Supervised Learning Based on Temporal Coding in Spiking Neural Networks
- (2017) Hesham Mostafa IEEE Transactions on Neural Networks and Learning Systems
- Encoding neural and synaptic functionalities in electron spin: A pathway to efficient neuromorphic computing
- (2017) Abhronil Sengupta et al. Applied Physics Reviews
- Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing
- (2016) Zhongrui Wang et al. NATURE MATERIALS
- Training Deep Spiking Neural Networks Using Backpropagation
- (2016) Jun Haeng Lee et al. Frontiers in Neuroscience
- Hybrid Spintronic-CMOS Spiking Neural Network with On-Chip Learning: Devices, Circuits, and Systems
- (2016) Abhronil Sengupta et al. Physical Review Applied
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Unsupervised learning of digit recognition using spike-timing-dependent plasticity
- (2015) Peter U. Diehl et al. Frontiers in Computational Neuroscience
- Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition
- (2014) Yongqiang Cao et al. INTERNATIONAL JOURNAL OF COMPUTER VISION
- Real-time classification and sensor fusion with a spiking deep belief network
- (2013) Peter O'Connor et al. Frontiers in Neuroscience
- Competitive STDP-Based Spike Pattern Learning
- (2009) Timothée Masquelier et al. NEURAL COMPUTATION
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started