By Cheng-Lin Liu, Amir Hussain, Bin Luo, Kay Chen Tan, Yi Zeng, Zhaoxiang Zhang
This publication constitutes the refereed lawsuits of the eighth foreign convention on mind encouraged Cognitive platforms, BICS 2016, held in Beijing, China, in November 2016. The 32 complete papers provided have been conscientiously reviewed and chosen from forty three submissions. They speak about the rising parts and demanding situations, current the cutting-edge of brain-inspired cognitive structures study and purposes in different fields by means of overlaying many issues in mind encouraged cognitive structures comparable learn together with biologically encouraged platforms, cognitive neuroscience, types realization, and neural computation.
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Extra info for Advances in Brain Inspired Cognitive Systems: 8th International Conference, BICS 2016, Beijing, China, November 28-30, 2016, Proceedings
Our experiments demonstrated that the new approach can generate traditional Chinese poems pretty well and even pass the Feigenbaum Test. 2 Related Work A multitude of methods have been proposed for poem automatic generation. The ﬁrst approach is based on rules and templates. For example, [15,18] employed a phrase search approach for Japanese poem generation, and  proposed an approach based on word association norms.  and  used semantic and grammar templates for Spanish poem generation.
5. Beliefs of their positions after diﬀerent random movements test. The three robots successfully passed all the 9 tests. Videos on the robots mirror self-recognition test is available at the Robot Self-consciousness Project page2 . 5 Conclusion With the long term goal of building a robot with consciousness, especially with self-consciousness, as a ﬁrst step, this paper provides an attempt to build a brain-inspired robot mirror neuron system model. Then we apply the model to robots with the purpose of passing the mirror self-recognition test.
A deep learning approach to machine transliteration. In: Proceedings of the Fourth Workshop on Statistical Machine Translation, pp. 233–241. Association for Computational Linguistics (2009) 10. : Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 3371–3408 (2010) Implementation of EEG Emotion Recognition System 33 11. : Deep belief networks. Scholarpedia 4(5), 5947 (2009) 12. : Emotion classiﬁcation based on gamma-band EEG.