System size resonance in attractor neural networks

Miguel A. de la Casa; Elka Korutcheva; Javier de la Rubia; Juan M. R. Parrondo

doi:10.1117/12.547053

25 May 2004 System size resonance in attractor neural networks

Miguel A. de la Casa, Elka Korutcheva, Javier de la Rubia, Juan M. R. Parrondo

Proceedings Volume 5471, Noise in Complex Systems and Stochastic Dynamics II; (2004) https://doi.org/10.1117/12.547053
Event: Second International Symposium on Fluctuations and Noise, 2004, Maspalomas, Gran Canaria Island, Spain

Abstract

We consider the dynamics of an attractor neural network of a finite size N, trained with two patterns, which is subject to the action of an external stimulus (or field). This field drives the system to one of the patterns or to another for alternating intervals of duration T. It is observed that, for not too strong fields, the response of the network to the evolving field is optimal for some finite size, decreasing for smaller or larger systems. This is the so-called system size resonance, already reported for the Ising model. The explanation of this results is related to the phenomenon of stochastic resonance.

Citation Download Citation

Miguel A. de la Casa, Elka Korutcheva, Javier de la Rubia, and Juan M. R. Parrondo "System size resonance in attractor neural networks", Proc. SPIE 5471, Noise in Complex Systems and Stochastic Dynamics II, (25 May 2004); https://doi.org/10.1117/12.547053

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