Elementary Probabilistic Cellular Automata with Memory in Cells

Author(s):  
Ramón Alonso-Sanz ◽  
Margarita Martín
2010 ◽  
Vol 21 (09) ◽  
pp. 1115-1128 ◽  
Author(s):  
RAMÓN ALONSO-SANZ

This article considers an extension to the standard framework of cellular automata by implementing memory capability in cells. It is shown that the important block HPP rule behaves as an excellent classifier of the density in the initial configuration when applied to cells endowed with pondered memory of their previous states. If the weighing is made so that the most recent state values are assigning the highest weights, the HPP rule surpasses the performance of the best two-dimensional density classifiers reported in the literature.


2014 ◽  
Vol 24 (09) ◽  
pp. 1450116 ◽  
Author(s):  
Shigeru Ninagawa ◽  
Andrew Adamatzky ◽  
Ramón Alonso-Sanz

We study elementary cellular automata with memory. The memory is a weighted function averaged over cell states in a time interval, with a varying factor which determines how strongly a cell's previous states contribute to the cell's present state. We classify selected cell-state transition functions based on Lempel–Ziv compressibility of space-time automaton configurations generated by these functions and the spectral analysis of their transitory behavior. We focus on rules 18, 22, and 54 because they exhibit the most intriguing behavior, including computational universality. We show that a complex behavior is observed near the nonmonotonous transition to null behavior (rules 18 and 54) or during the monotonic transition from chaotic to periodic behavior (rule 22).


Complexity ◽  
2014 ◽  
Vol 20 (1) ◽  
pp. 49-56 ◽  
Author(s):  
Ramón Alonso-Sanz

2014 ◽  
Vol 559 ◽  
pp. 42-72 ◽  
Author(s):  
Jean Mairesse ◽  
Irène Marcovici

2018 ◽  
Vol 174 (3-4) ◽  
pp. 1187-1217 ◽  
Author(s):  
Alexander E. Holroyd ◽  
Irène Marcovici ◽  
James B. Martin

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