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020 _a9783031486555
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024 7 _a10.1007/978-3-031-48655-5
_2doi
050 4 _aQC350-467
072 7 _aPHJ
_2bicssc
072 7 _aSCI053000
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100 1 _aBile, Alessandro.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_968415
245 1 0 _aSolitonic Neural Networks
_h[electronic resource] :
_bAn Innovative Photonic Neural Network Based on Solitonic Interconnections /
_cby Alessandro Bile.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aX, 105 p. 54 illus., 44 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aMachine Intelligence for Materials Science,
_x2948-1821
505 0 _aIntroduction to Neural Networks: Biological Neural Network -- Overview of neuromorphic optical systems -- Towards neuro-learning process: pyschomemories -- The solitonic X-Junction as a photonic neuron -- Solitonic Neural Network acting as an episodic memory.
520 _aThis book describes the realization of an intelligent all-optical system that can replicate the functional building blocks of the biological brain. Starting with an analysis of biological neuronal dynamics and traversing the state of the art of neuromorphic systems developed to date, the book arrives at a description of neural networks realized through spatial soliton technology. After a brief introduction to the biology of neural networks (Chapter 1), the book delves into the description of the neuromorphic problem emphasizing the peculiarities of optical hardware developed to date. (Chapter 2). Chapter 3 is dedicated to the description of psychomemories , which represent the modeling of human learning according to the theories of modern neuro-psychology. This chapter provides the prerequisites for understanding how solitonic neural networks (SNNs) are able to learn and how they approach biological models. Chapter 4 focuses on the experimentation of solitonic optic neurons in thin layers of lithium niobate. Optical techniques for supervised and unsupervised learning are discussed. The entire chapter is accompanied by theoretical, simulative and experimental results. This chapter explains how an X-junction neuron is able to establish synapses, modify them, or erase them. The erasure of solitonic structures represents an important innovation in the field of nonlinear optics. Finally, Chapter 5 reports on the implementation of a network of neurons capable of processing information and storing it exactly as a human episodic memory does. The chapter ends with a number of insights into the lines of research that are currently being pursued on the basis of the results obtained. The book is meant for graduate students and researchers in the fields of optics, photonic applications, and biology. However, the main beneficiaries of this book are senior researchers in the field of nonlinear optics and artificial intelligence.To fully understand the results, it is important to have a basic knowledge of optical physics and neuron biology.
650 0 _aOptics.
_966607
650 0 _aNonlinear Optics.
_968416
650 0 _aArtificial intelligence.
_958441
650 0 _aBiomaterials.
650 1 4 _aOptics and Photonics.
_967319
650 2 4 _aNonlinear Optics.
_968416
650 2 4 _aArtificial Intelligence.
_958441
650 2 4 _aBiomaterials.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031486548
776 0 8 _iPrinted edition:
_z9783031486562
776 0 8 _iPrinted edition:
_z9783031486579
830 0 _aMachine Intelligence for Materials Science,
_x2948-1821
_968417
856 4 0 _uhttps://doi.org/10.1007/978-3-031-48655-5
912 _aZDB-2-PHA
912 _aZDB-2-SXP
999 _c56872
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