東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Solitonic neural networks = an innovative photonic neural network based on solitonic interconnections /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Solitonic neural networks/ by Alessandro Bile.
其他題名:	an innovative photonic neural network based on solitonic interconnections /
作者:	Bile, Alessandro.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	x, 105 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Neural networks (Computer science) -
電子資源:	https://doi.org/10.1007/978-3-031-48655-5
ISBN:	9783031486555

Solitonic neural networks = an innovative photonic neural network based on solitonic interconnections /
Bile, Alessandro.

Solitonic neural networksan innovative photonic neural network based on solitonic interconnections /[electronic resource] :by Alessandro Bile. - Cham :Springer Nature Switzerland :2024. - x, 105 p. :ill., digital ;24 cm. - Machine intelligence for materials science,2948-1821. - Machine intelligence for materials science..

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.

ISBN: 9783031486555

Standard No.: 10.1007/978-3-031-48655-5doiSubjects--Topical Terms:

532070
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

Solitonic neural networks = an innovative photonic neural network based on solitonic interconnections /
LDR:03403nmm a2200337 a 4500 001 2368267
003 DE-He213
005 20231221073505.0
006 m d
007 cr nn 008maaau
008 241127s2024 sz s 0 eng d
020 $a 9783031486555 $q (electronic bk.)
020 $a 9783031486548 $q (paper)
024 7 $a 10.1007/978-3-031-48655-5 $2 doi
035 $a 978-3-031-48655-5
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.87
072 7 $a PHJ $2 bicssc
072 7 $a SCI053000 $2 bisacsh
072 7 $a PHJ $2 thema
082 0 4 $a 006.32 $2 23
090 $a QA76.87 $b .B595 2024
100 1 $a Bile, Alessandro. $3 3712107
245 1 0 $a Solitonic neural networks $h [electronic resource] : $b an innovative photonic neural network based on solitonic interconnections / $c by Alessandro Bile.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2024.
300 $a x, 105 p. : $b ill., digital ; $c 24 cm.
490 1 $a Machine intelligence for materials science, $x 2948-1821
505 0 $a 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.
520 $a 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.
650 0 $a Neural networks (Computer science) $3 532070
650 0 $a Solitons. $3 658940
650 1 4 $a Optics and Photonics. $3 1060604
650 2 4 $a Nonlinear Optics. $3 3538901
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Biomaterials. $3 898413
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Machine intelligence for materials science. $3 3712108
856 4 0 $u https://doi.org/10.1007/978-3-031-48655-5
950 $a Physics and Astronomy (SpringerNature-11651)