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'''Neuro-Cellular Automata'''
'''Neuro-Cellular Automata'''


A new type of Cellular Automata is presented and studied. On this automata, the standard way of representing the rules is replaced by an Artificial Neural Network, thus having a more biological inspired automata. It is described the motivation for the development for this kind of automata, as well as, the study that shows its development. The study uses the simple 1D Cellular Automata, enabling an understanding of the needed complexity for the Neural Networks, how to evaluate the automata by using complexity measures, as well as the evolution, of simple Neuro-Cellular Automata, as means for the design of such systems.
A new type of Cellular Automata is presented and studied: the Neuro-Cellular Automata. The standard way of representing the rules of an automaton is replaced by an Artificial Neural Network. The motivation of this change is two-fold: to have a more biologically inspired automata and to explore the connections between Cellular Automata, Neural Networks, Evolution and Complexity. The process of the development of this kind of automata is also described, as well as some considerations about important aspects of the work. We use simple one-dimensional Cellular Automata, in order to better understand the complexity of the different components of the Neuro-Cellular Automata and to evolve it, because evolution is required to design these type of automata. A study regarding ways to measure complexity is also performed, since these measures play an important role in evaluating the population of automata in the evolutionary process.




Revision as of 16:04, 7 September 2006

CSSS 2006 Santa Fe

Neuro-Cellular Automata

A new type of Cellular Automata is presented and studied: the Neuro-Cellular Automata. The standard way of representing the rules of an automaton is replaced by an Artificial Neural Network. The motivation of this change is two-fold: to have a more biologically inspired automata and to explore the connections between Cellular Automata, Neural Networks, Evolution and Complexity. The process of the development of this kind of automata is also described, as well as some considerations about important aspects of the work. We use simple one-dimensional Cellular Automata, in order to better understand the complexity of the different components of the Neuro-Cellular Automata and to evolve it, because evolution is required to design these type of automata. A study regarding ways to measure complexity is also performed, since these measures play an important role in evaluating the population of automata in the evolutionary process.


Interested participants: Jorge Tavares, Cornelia Kreutzer, Anna Fedor

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