Optical learning neural networks with two-dimensional structure

Masahiko Mori; Toyohiko Yatagai

doi:10.1117/12.304948

1 April 1998 Optical learning neural networks with two-dimensional structure

Masahiko Mori, Toyohiko Yatagai

Proceedings Volume 3402, Optical Information Science and Technology (OIST97): Optical Memory and Neural Networks; (1998) https://doi.org/10.1117/12.304948
Event: Optical Information Science and Technology, 1997, Moscow, Russian Federation

Abstract

We constructed optical systems for learning neural networks with 2D structure sing Selfoc microlens arrays. On the systems, pattern recognitions of typed alphabetic characters which were directly detected with a CCD camera were realized. Liquid crystal devices, an electron-beam addressed spatial light modulator and a Pockels readout optical modulator were used for displaying weight matrices of the neural networks. The weights were renewed according to the random search algorithm or the delta rule with error signals calculated optically. The 2D structure for image processing can be implemented with large scale networks that consist of several thousands input neurons.

Citation Download Citation

Masahiko Mori and Toyohiko Yatagai "Optical learning neural networks with two-dimensional structure", Proc. SPIE 3402, Optical Information Science and Technology (OIST97): Optical Memory and Neural Networks, (1 April 1998); https://doi.org/10.1117/12.304948

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available