Special Issue of BICS 2016

doi:10.1007/s12559-018-9551-3

	Special Issue of BICS 2016
	Liu, Cheng-Lin1 ; Hussain, Amir 2; Luo, Bin 3; Tan, Kay Chen 4; Zeng, Yi1 ; Zhang, Zhaoxiang1
刊名	COGNITIVE COMPUTATION
	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
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/21593]
专题	自动化研究所_类脑智能研究中心
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.