Special Issue of BICS 2016
文献类型：期刊论文
| 作者 | Liu, Cheng-Lin1 ; Hussain, Amir2; Luo, Bin3; Tan, Kay Chen4; Zeng, Yi1; Zhang, Zhaoxiang1
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| 刊名 | COGNITIVE COMPUTATION
 |
| 出版日期 | 2018-04-01 |
| 卷号 | 10期号:2页码:282-283 |
| 关键词 | Bics Brain-inspired Artificial Intelligence Deep Neural Networks |
| DOI | 10.1007/s12559-018-9551-3 |
| 文献子类 | Editorial Material |
| 英文摘要 | Brain-inspired cognitive models and algorithms are important components driving artificial intelligence (AI). Deep neural networks are currently considered the most effective models to yield high perception and inference performance by learning from big data. However they manifest inferior generalization, robustness, interpretability, and adaptability when compared to the human brain. Despite neural circuits and cognition mechanisms of the brain having many unknowns, they continue to inspire AI in different ways. The International Conference on Brain Inspired Cognitive System (BICS) has been organized since 2004 to stimulate interdisciplinary research and exchanges in brain-inspired cognitive systems and applications in diverse fields. The 8th International Conference on Brain Inspired Cognitive System (BICS 2016) was held in Beijing, China, November 28–30, 2016. This special issue aims to report new advances since BICS 2016, by including expanded versions of selected conference papers and also new contributions. Until April 20, 2017, the special issue received 18 submissions, most of which were expanded versions of BICS 2016 conference papers, along with a few new submissions. Following a rigorous peer review process, nine papers were accepted for publication in this special issue. The nine papers present contributions in brain information processing, braininspired cognitive models, and algorithms for decision, learning, vision, and applications. In BAnatomical Pattern Analysis for Decoding Visual Stimuli in Human Brains,^ Yousefnezhad and Zhang propose Anatomical Pattern Analysis (APA) for decoding visual stimuli in the human brain. This framework develops a novel anatomical feature extraction method and a new imbalance AdaBoost algorithm for binary classification. Further, it utilizes an Error-Correcting Output Codes (ECOC) method for multiclass prediction. APA can automatically detect active regions for each category of the visual stimuli. Moreover, it enables us to combine homogeneous datasets for applying advanced classification. Experiments on four visual categories in fMRI data demonstrate the effectiveness of the proposed method. |
| WOS研究方向 | Computer Science ; Neurosciences & Neurology |
| 语种 | 英语 |
| WOS记录号 | WOS:000430190600008 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/21593]  |
| 专题 | 类脑智能研究中心_类脑认知计算 |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 2.Univ Stirling, Stirling, Scotland 3.Anhui Univ, Hefei, Anhui, Peoples R China 4.City Univ Hong Kong, Kowloon Tong, Hong Kong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Cheng-Lin,Hussain, Amir,Luo, Bin,et al. Special Issue of BICS 2016[J]. COGNITIVE COMPUTATION,2018,10(2):282-283. |
| APA | Liu, Cheng-Lin,Hussain, Amir,Luo, Bin,Tan, Kay Chen,Zeng, Yi,&Zhang, Zhaoxiang.(2018).Special Issue of BICS 2016.COGNITIVE COMPUTATION,10(2),282-283. |
| MLA | Liu, Cheng-Lin,et al."Special Issue of BICS 2016".COGNITIVE COMPUTATION 10.2(2018):282-283. |
入库方式： OAI收割
来源：自动化研究所
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