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浏览/检索结果: 共5条，第1-5条 帮助 限定条件 发表日期：2020    内容类型：期刊论文     已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 Temporal-Sequential Learning With a Brain-Inspired Spiking Neural Network and Its Application to Musical Memory 期刊论文 FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, 2020, 卷号: 14, 期号: 0, 页码: 51 作者:  Liang, Qian;  Zeng, Yi;  Xu, Bo 收藏  |  浏览/下载：27/0  |  提交时间：2020/08/24 spiking neural network  sequential memory  episodic memory  spike-timing-dependent plasticity  time perception  musical learning   Emergence and evolution of highly pathogenic porcine epidemic diarrhea virus by natural recombination of a low pathogenic vaccine isolate and a highly pathogenic strain in the spike gene 期刊论文 VIRUS EVOLUTION, 2020, 卷号: 6, 期号: 2, 页码: 10 作者:  Wang, Huinan;  Zhang, Libo;  Shang, Yuanbin;  Tan, Rongrong;  Ji, Mingxiang 收藏  |  浏览/下载：293/0  |  提交时间：2021/05/24 porcine epidemic diarrhea virus  evolution  recombination  pathogenicity  spike gene   Selaginella was hyperdiverse already in the Cretaceous 期刊论文 NEW PHYTOLOGIST, 2020, 页码: 7 作者:  Schmidt, Alexander R.;  Regalado, Ledis;  Weststrand, Stina;  Korall, Petra;  Sadowski, Eva-Maria 收藏  |  浏览/下载：40/0  |  提交时间：2020/10/28 amber  Cretaceous  Cretaceous Terrestrial Revolution  lycophytes  Mesozoic  Selaginella  Selaginellales  spike moss   Crude oil price analysis and forecasting: A perspective of "new triangle" 期刊论文 ENERGY ECONOMICS, 2020, 卷号: 87, 页码: 14 作者:  Lu, Quanying;  Li, Yuze;  Chai, Jian;  Wang, Shouyang 收藏  |  浏览/下载：64/0  |  提交时间：2020/06/30 Crude oil  Dynamic Bayesian structural time series model  Google trend  Kalman filtering  Spike and slab prior  Bayesian model average   A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule 期刊论文 NEURAL NETWORKS, 2020, 卷号: 121, 期号: 2, 页码: 387-395 作者:  Hao, Yunzhe;  Huang, Xuhui;  Dong, Meng;  Xu, Bo 收藏  |  浏览/下载：12/0  |  提交时间：2020/03/30 Spiking neural networks  Dopamine-modulated spike-timing dependent plasticity  Pattern recognition  Supervised learning  Biologically plausibility