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Deep Learning and Deep Knowledge Representation in the Human Brain

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Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence

Part of the book series: Springer Series on Bio- and Neurosystems ((SSBN,volume 7))

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Abstract

Spiking neural networks (SNN) and the deep learning algorithms for them have been inspired by the structure, the organisation and the many aspects of deep learning and deep knowledge representation in the human brain. This chapter presents basic information about brain structures and functions and reveals some inner processes of deep learning and deep knowledge representation as inspiration for brain-inspired SNN (BI-SNN) and brain-inspired AI (BI-AI) presented in the next chapters. The presented here information is not intended for modeling the brain in its precise structural and functional complexity, but rather for: (1) Borrowing spatio-temporal information processing principles from the brain for the creation of brain-inspired SNN and brain-inspired AI as general spatio-temporal data machines for deep learning and deep knowledge representation in time-space; (2) Understanding brain data, when modeled with SNN, for a more accurate analysis and for a better understanding of the brain processes that generated the data.

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Acknowledgements

Some of the text in this chapter is adopted from [7] and some figures are adopted from [7, 70]. I am highly indebted to Lubica Benuskova as my co-author of the Springer book [7], who contributed a great deal to the book and indirectly—to this chapter.

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  1. Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland University of Technology, Auckland, New Zealand

    Nikola K. Kasabov

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Correspondence to Nikola K. Kasabov .

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Kasabov, N.K. (2019). Deep Learning and Deep Knowledge Representation in the Human Brain. In: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence . Springer Series on Bio- and Neurosystems, vol 7. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-57715-8_3

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