04393nam a22005655i 4500 978-3-031-48655-5 DE-He213 20251030110858.0 cr nn 008mamaa 231221s2024 sz | s |||| 0|eng d 9783031486555 978-3-031-48655-5 10.1007/978-3-031-48655-5 doi QC350-467 PHJ bicssc SCI053000 bisacsh PHJ thema Bile, Alessandro. author. aut http://id.loc.gov/vocabulary/relators/aut 68415 Solitonic Neural Networks [electronic resource] : An Innovative Photonic Neural Network Based on Solitonic Interconnections / by Alessandro Bile. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. X, 105 p. 54 illus., 44 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Machine Intelligence for Materials Science, 2948-1821 Introduction 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. This 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. Optics. 66607 Nonlinear Optics. 68416 Artificial intelligence. 58441 Biomaterials. Optics and Photonics. 67319 Nonlinear Optics. 68416 Artificial Intelligence. 58441 Biomaterials. SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031486548 Printed edition: 9783031486562 Printed edition: 9783031486579 Machine Intelligence for Materials Science, 2948-1821 68417 https://doi.org/10.1007/978-3-031-48655-5 ZDB-2-PHA ZDB-2-SXP 56872 56872