机构

• 自动化研究所 [10]
• 计算技术研究所 [2]

采集方式

• OAI收割 [12]

内容类型

• 期刊论文 [10]
• 会议论文 [2]

发表日期

• 2021 [12]

学科主题

筛选

浏览/检索结果: 共12条，第1-10条 帮助 限定条件 存缴方式：oaiharvest    发表日期：2021     条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择提交时间升序提交时间降序题名升序题名降序作者升序作者降序发表日期升序发表日期降序 Toward a Brain-Inspired Developmental Neural Network Based on Dendritic Spine Dynamics 期刊论文  OAI收割 NEURAL COMPUTATION, 2021, 卷号: 34, 期号: 1, 页码: 172-189 作者:  Zhao, Feifei;  Zeng, Yi;  Bai, Jun   |  收藏  |  浏览/下载：8/0  |  提交时间：2022/01/27 A Brain-Inspired Approach for Collision-Free Movement Planning in the Small Operational Space 期刊论文  OAI收割 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 12 作者:  Xing, Dengpeng;  Li, Jiale;  Zhang, Tielin;  Xu, Bo   |  收藏  |  浏览/下载：22/0  |  提交时间：2022/01/27 Visualization  Cameras  Planning  Task analysis  Neurons  Collision avoidance  Biology  Brain-inspired structure  collision-free movement planning  small operational space  spiking neural networks (SNNs)   Exploring Adversarial Attack in Spiking Neural Networks With Spike-Compatible Gradient 期刊论文  OAI收割 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 15 作者:  Liang, Ling;  Hu, Xing;  Deng, Lei;  Wu, Yujie;  Li, Guoqi   |  收藏  |  浏览/下载：21/0  |  提交时间：2022/06/21 Spatiotemporal phenomena  Computational modeling  Perturbation methods  Biological neural networks  Backpropagation  Unsupervised learning  Training  Adversarial attack  backpropagation through time (BPTT)  neuromorphic computing  spike-compatible gradient  spiking neural networks (SNNs)   A Brain-Inspired Causal Reasoning Model Based on Spiking Neural Networks 会议论文  OAI收割 Shenzhen, China, 18-22 July 2021 作者:  Fang HongJian(方宏坚);  Zeng Yi(曾毅)   |  收藏  |  浏览/下载：14/0  |  提交时间：2022/09/26 Quantum superposition inspired spiking neural network 期刊论文  OAI收割 ISCIENCE, 2021, 卷号: 24, 期号: 8, 页码: 16 作者:  Sun, Yinqian;  Zeng, Yi;  Zhang, Tielin   |  收藏  |  浏览/下载：26/0  |  提交时间：2021/11/03 Tuning Convolutional Spiking Neural Network With Biologically Plausible Reward Propagation 期刊论文  OAI收割 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 11 作者:  Zhang, Tielin;  Jia, Shuncheng;  Cheng, Xiang;  Xu, Bo   |  收藏  |  浏览/下载：22/0  |  提交时间：2022/01/27 Neurons  Biology  Convolution  Biological neural networks  Tuning  Membrane potentials  Kernel  Biologically plausible computing  neuronal dynamics  reward propagation  spiking neural network (SNN)   Dynamically Optimizing Network Structure Based on Synaptic Pruning in the Brain 期刊论文  OAI收割 FRONTIERS IN SYSTEMS NEUROSCIENCE, 2021, 卷号: 15, 页码: 8 作者:  Zhao, Feifei;  Zeng, Yi   |  收藏  |  浏览/下载：26/0  |  提交时间：2021/08/15 synaptic pruning  developmental neural network  optimizing network structure  accelerating learning  compressing network   Bioinspired Gain-Modulated Recurrent Neural Network for Controlling Musculoskeletal Robot 期刊论文  OAI收割 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 16 作者:  Zhong, Shanlin;  Zhou, Junjie;  Qiao, Hong   |  收藏  |  浏览/下载：12/0  |  提交时间：2022/01/27 Robots  Modulation  Robot kinematics  Neurons  Brain modeling  Recurrent neural networks  Encoding  Biologically inspired control  gain modulation  motor primitives  musculoskeletal robot  recurrent neural network (RNN)   Convolutional Ordinal Regression Forest for Image Ordinal Estimation 期刊论文  OAI收割 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 12 作者:  Zhu, Haiping;  Shan, Hongming;  Zhang, Yuheng;  Che, Lingfu;  Xu, Xiaoyang   |  收藏  |  浏览/下载：11/0  |  提交时间：2022/01/27 Decision trees  Estimation  Task analysis  Forestry  Vegetation  Random forests  Support vector machines  Differentiable decision trees  image ordinal estimation  ordinal distribution learning  ordinal regression (OR)  random forest   Brain Inspired Sequences Production by Spiking Neural Networks With Reward-Modulated STDP 期刊论文  OAI收割 FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, 2021, 卷号: 15, 页码: 13 作者:  Fang, Hongjian;  Zeng, Yi;  Zhao, Feifei   |  收藏  |  浏览/下载：31/0  |  提交时间：2021/04/06 brain-inspired intelligence  spiking neural network  reward-medulated STDP  population coding  reinforcement learning