04190nam a22005175i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001400172072001600186072002300202072001500225100008600240245015300326250001800479264006900497300006300566336002600629337002600655338003600681347002400717490005900741505026400800520206501064650001903129650002903148650003603177650001803213650003303231650002903264650003603293650001803329710003403347773002603381776003603407776003603443776003603479830006603515856004603581912001403627912001403641999001703655978-3-031-48655-5DE-He21320251030110858.0cr nn 008mamaa231221s2024    sz |    s    |||| 0|eng d  a97830314865559978-3-031-48655-57 a10.1007/978-3-031-48655-52doi 4aQC350-467 7aPHJ2bicssc 7aSCI0530002bisacsh 7aPHJ2thema1 aBile, Alessandro.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut96841510aSolitonic Neural Networksh[electronic resource] :bAn Innovative Photonic Neural Network Based on Solitonic Interconnections /cby Alessandro Bile.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aX, 105 p. 54 illus., 44 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aMachine Intelligence for Materials Science,x2948-18210 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.  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. 0aOptics.966607 0aNonlinear Optics.968416 0aArtificial intelligence.958441 0aBiomaterials.14aOptics and Photonics.96731924aNonlinear Optics.96841624aArtificial Intelligence.95844124aBiomaterials.2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303148654808iPrinted edition:z978303148656208iPrinted edition:z9783031486579 0aMachine Intelligence for Materials Science,x2948-182196841740uhttps://doi.org/10.1007/978-3-031-48655-5  aZDB-2-PHA  aZDB-2-SXP  c56872d56872