IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Bioinspired Associative Memories

Bioinspired Associative Memories
View Sample PDF
Author(s): Roberto A. Vazquez (University of Valladolid, Spain)and Humberto Sossa (Center for Computing Research, IPN, Mexico)
Copyright: 2009
Pages: 8
Source title: Encyclopedia of Artificial Intelligence
Source Author(s)/Editor(s): Juan Ramón Rabuñal Dopico (University of A Coruña, Spain), Julian Dorado (University of A Coruña, Spain)and Alejandro Pazos (University of A Coruña, Spain)
DOI: 10.4018/978-1-59904-849-9.ch039

Purchase

View Bioinspired Associative Memories on the publisher's website for pricing and purchasing information.

Abstract

An associative memory AM is a special kind of neural network that allows recalling one output pattern given an input pattern as a key that might be altered by some kind of noise (additive, subtractive or mixed). Most of these models have several constraints that limit their applicability in complex problems such as face recognition (FR) and 3D object recognition (3DOR). Despite of the power of these approaches, they cannot reach their full power without applying new mechanisms based on current and future study of biological neural networks. In this direction, we would like to present a brief summary concerning a new associative model based on some neurobiological aspects of human brain. In addition, we would like to describe how this dynamic associative memory (DAM), combined with some aspects of infant vision system, could be applied to solve some of the most important problems of pattern recognition: FR and 3DOR.

Related Content

Kamel Mouloudj, Vu Lan Oanh LE, Achouak Bouarar, Ahmed Chemseddine Bouarar, Dachel Martínez Asanza, Mayuri Srivastava. © 2024. 20 pages.
José Eduardo Aleixo, José Luís Reis, Sandrina Francisca Teixeira, Ana Pinto de Lima. © 2024. 52 pages.
Jorge Figueiredo, Isabel Oliveira, Sérgio Silva, Margarida Pocinho, António Cardoso, Manuel Pereira. © 2024. 24 pages.
Fatih Pinarbasi. © 2024. 20 pages.
Stavros Kaperonis. © 2024. 25 pages.
Thomas Rui Mendes, Ana Cristina Antunes. © 2024. 24 pages.
Nuno Geada. © 2024. 12 pages.
Body Bottom