Advances in Brain Inspired Cognitive Systems
This special issue focuses on recent advancements in the field of brain inspired cognitive systems. It comprises 18 articles which are carefully selected, significantly revised versions of papers presented at the seventh International Conference on Brain Inspired Cognitive Systems (BICS 2015) held at Hefei, China, on December 11 13, 2015. The aim of BICS 2015 was to bring together leading scientists, engineers, and educators who use analytic, syntactic, and computational methods both to understand the prodigious processing properties of biological systems and, specifically, of the brain, and to exploit such knowledge to advance computational methods toward higher levels of cognitive competence. The conference featured plenary speeches given by renowned scholars and a range of technical sessions focusing on timely topics of interest to the scientific community. Based on the recommendation of symposium organizers and reviewers, a number of authors were invited to submit revised versions of their contributions to this special issue. All articles went through a rigorous review procedure involving at least three independent experts before being accepted for publication. Chen et al. propose an efficient algorithm for actionbased pedestrian identification using hierarchical matching pursuit and order preserving sparse coding to identify and classify features. Gepperth et al. present a novel system for performing multi sensor fusion with experience based learning. The authors demonstrate that near optimal fusion can be learned and is a resource efficient alternative to empirical estimation of joint probability distributions and Bayesian inference. They also show that their generative learning approach outperforms Bayesian optimum as it is capable of rejecting outliers through detection of systematic changes in input statistics. Mi et al. describe a novel system for occluded face recognition. They propose a new similarity matrix called 'averaged degree of aggregation of matched pixels' and show that their system is very robust against occlusion and competitive in terms of recognition accuracy and computation time. Jiang et al. present a cognitively distributed simultaneous localization and tracking algorithm, with applications in Gaussian distributed wireless sensor networks, based on adaptive distributed filtering for target tracking and sensor localization. Their proposed system shows higher accuracy in parameter estimation, coupled with low computational complexity. Zhao et al. propose a new method called 'elastic matching' for common visual pattern discovery, through inducing sparse solutions and conducting detection more robustly. Zhang et al. describe a discriminative Lasso in which sparsity and correlation are jointly considered. The method Amir Hussain ahu@cs.stir.ac.uk
Cognitive Computation