Abstract
In this paper, a morphological neural network (MNN) cognitive tree model related to multi disciplines is proposed. The model has four layers: soil layer, primary layer, growth layer and presentation layer. Through the study of MNN at different levels, the cognitive function and mechanism of MNN are profoundly revealed, and the theoretical framework of MNN cognition is established. This paper can be seen as an example of multi-disciplinary crossover and fusion research, which not only helps to improve the understanding of MNN itself, but also brings some inspirations to promote the interdisciplinary research and coordinated development of computer science, artificial intelligence, neurobiology, cognitive psychology and so on.
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Acknowledgements
This work is supported by the Henan Province’s Key R&D Project (Grant No. 192102310217), the science and technology research project of Zhengzhou city (Grant No. 153PKJGG153), the Key Research Project of Zhengzhou University of Industrial Technology (Grant No. JG-190101), the Key Scientific Research Projects of Higher Education Institutions of Henan Province (Grant No. 20A520039), and the Training Project of Young Backbone Teachers of Henan Province (Grant No. 2019GGJS279).
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Feng, N., Qin, L., Sun, B. (2020). A Cognitive Model of Morphological Neural Network. In: Huang, DS., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12463. Springer, Cham. https://doi.org/10.1007/978-3-030-60799-9_10
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