The Remarkable Robustness of Surrogate Gradient Learning for Instilling Complex Function in Spiking Neural Networks
出版年份 2021 全文链接
标题
The Remarkable Robustness of Surrogate Gradient Learning for Instilling Complex Function in Spiking Neural Networks
作者
关键词
-
出版物
NEURAL COMPUTATION
Volume -, Issue -, Pages 1-27
出版商
MIT Press - Journals
发表日期
2021-01-30
DOI
10.1162/neco_a_01367
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Bridging large-scale neuronal recordings and large-scale network models using dimensionality reduction
- (2019) Ryan C. Williamson et al. CURRENT OPINION IN NEUROBIOLOGY
- Analyzing biological and artificial neural networks: challenges with opportunities for synergy?
- (2019) David GT Barrett et al. CURRENT OPINION IN NEUROBIOLOGY
- Local online learning in recurrent networks with random feedback
- (2019) James M Murray eLife
- 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
- A deep learning framework for neuroscience
- (2019) Blake A. Richards et al. NATURE NEUROSCIENCE
- SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks
- (2018) Friedemann Zenke et al. NEURAL COMPUTATION
- Motor primitives in space and time via targeted gain modulation in cortical networks
- (2018) Jake P. Stroud et al. NATURE NEUROSCIENCE
- 'Artiphysiology' reveals V4-like shape tuning in a deep network trained for image classification
- (2018) Dean A Pospisil et al. eLife
- Deep Learning With Spiking Neurons: Opportunities and Challenges
- (2018) Michael Pfeiffer et al. Frontiers in Neuroscience
- Flexible timing by temporal scaling of cortical responses
- (2017) Jing Wang et al. NATURE NEUROSCIENCE
- Using goal-driven deep learning models to understand sensory cortex
- (2016) Daniel L K Yamins et al. NATURE NEUROSCIENCE
- Convolutional networks for fast, energy-efficient neuromorphic computing
- (2016) Steven K. Esser et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Spiking neurons can discover predictive features by aggregate-label learning
- (2016) R. Gutig SCIENCE
- Training Deep Spiking Neural Networks Using Backpropagation
- (2016) Jun Haeng Lee et al. Frontiers in Neuroscience
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Deep learning in neural networks: An overview
- (2015) Jürgen Schmidhuber NEURAL NETWORKS
- Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades
- (2015) Garrick Orchard et al. Frontiers in Neuroscience
- Performance-optimized hierarchical models predict neural responses in higher visual cortex
- (2014) D. L. K. Yamins et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Context-dependent computation by recurrent dynamics in prefrontal cortex
- (2013) Valerio Mante et al. NATURE
- Opening the Black Box: Low-Dimensional Dynamics in High-Dimensional Recurrent Neural Networks
- (2012) David Sussillo et al. NEURAL COMPUTATION
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