Evolution of Cellular-automaton-based Associative Memories

A Probabilistic Cellular Automaton for Evolution

Journal de Physique I ◽

10.1051/jp1:1995186 ◽

1995 ◽

Vol 5 (9) ◽

pp. 1129-1134 ◽

Cited By ~ 2

Author(s):

Nikolaus Rajewsky ◽

Michael Schreckenberg

Keyword(s):

Cellular Automaton ◽

Probabilistic Cellular Automaton

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Spin glass model for a neural network : associative memories stored with unequal weights

Journal de Physique ◽

10.1051/jphys:01988004902016700 ◽

1988 ◽

Vol 49 (2) ◽

pp. 167-174 ◽

Cited By ~ 13

Author(s):

L. Viana

Keyword(s):

Neural Network ◽

Spin Glass ◽

Associative Memories ◽

Spin Glass Model ◽

Glass Model

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Autómatas Celulares Aplicados al Comportamiento de Células de Cáncer Cervicouterino

Tecnología Educativa Revista CONAIC ◽

10.32671/terc.v6i1.78 ◽

2019 ◽

Vol 6 (1) ◽

pp. 44-49

Author(s):

Tania Muñoz Jiménez ◽

Aurora Torres Soto ◽

María Dolores Torres Soto

Keyword(s):

Cellular Automaton ◽

Medical Model ◽

Research Process ◽

Simulation Algorithm ◽

Stages Of Development ◽

Literary Research ◽

Migration Dynamics ◽

Three Stages ◽

And Migration ◽

Best Parameters

En este documento se describe el desarrollo e implementación de un modelo para simular computacionalmente la dinámica del crecimiento y migración del cáncer cervicouterino, considerando sus principales características: proliferación, migración y necrosis, así como sus etapas de desarrollo. El modelo se desarrolló mediante un autómata celular con enfoques paralelo y secuencial. El autómata celular se basó en el modelo de Gompertz para simular las etapas de desarrollo de este cáncer, el cual se dividió en tres etapas cada una con diferentes comportamientos durante la simulación. Se realizó un diseño experimental con parámetros de entrada que se seleccionaron a partir de la investigación literaria y su discusión con médicos expertos. Al final del proceso de investigación, se logró obtener un algoritmo computacional de simulación muy bueno comparado con el modelo médico de Gompertz y se encontraron los mejores parámetros para su ejecución mediante un diseño factorial soportado estadísticamente. This paper describes the development and implementation of a model to computationally simulate the growth and migration dynamics of cervical cancer, considering its main characteristics: proliferation, migration and necrosis, as well as its stages of development. The model was developed by means of a cellular automaton with parallel and sequential approaches. The cellular automaton was based on the model of Gompertz to simulate the stages of development of this cancer, which was divided into three stages, each with different behaviors during the simulation. An experimental design was carried out with input parameters that were selected from literary research and its discussion with expert physicians. At the end of the research process, a very good simulation algorithm was obtained compared to the Gompertz medical model and the best parameters for its execution were found by means of a statistically supported factorial design.

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Replication of a Binary Image on a One-Dimensional Cellular Automaton with Linear Rules

Complex Systems ◽

10.25088/complexsystems.27.4.415 ◽

2018 ◽

Vol 27 (4) ◽

pp. 415-430

Author(s):

U Srinivasa Rao ◽

Jeganathan L ◽

Keyword(s):

Cellular Automaton ◽

Binary Image ◽

One Dimensional

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Simulating Self-Regeneration and Self-Replication Processes Using Movable Cellular Automata with a Mutual Equilibrium Neighborhood

Complex Systems ◽

10.25088/complexsystems.29.4.741 ◽

2020 ◽

Vol 29 (4) ◽

pp. 741-757

Author(s):

Kateryna Hazdiuk ◽

◽

Volodymyr Zhikharevich ◽

Serhiy Ostapov ◽

◽

...

Keyword(s):

Cellular Automata ◽

Cellular Automaton ◽

Three Dimensional ◽

Computer Models ◽

The Self ◽

Model Construction ◽

Natural Phenomena ◽

Different Types ◽

Movable Cellular Automata ◽

Self Replication

This paper deals with the issue of model construction of the self-regeneration and self-replication processes using movable cellular automata (MCAs). The rules of cellular automaton (CA) interactions are found according to the concept of equilibrium neighborhood. The method is implemented by establishing these rules between different types of cellular automata (CAs). Several models for two- and three-dimensional cases are described, which depict both stable and unstable structures. As a result, computer models imitating such natural phenomena as self-replication and self-regeneration are obtained and graphically presented.

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