New properties of 2D Cellular Automata found through Polynomial Cellular Neural Networks

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dc.contributor.authorGiovanni E. Pazienza
dc.contributor.authorE. Gómez-Ramı́rez
dc.coverage.spatialBolivia
dc.date.accessioned2026-03-22T15:09:37Z
dc.date.available2026-03-22T15:09:37Z
dc.date.issued2009
dc.descriptionCitaciones: 5
dc.description.abstractIn this paper we show how polynomial cellular neural networks can be used to find new properties of two-dimensional binary cellular automata (CA). In particular, we define formally a complexity index for totalistic and semi-totalistic CA, and we discuss on the intrinsic complexity of universal CA finding a surprising result: universal rules are slightly more complex than linearly separable ones.
dc.identifier.doi10.1109/ijcnn.2009.5178977
dc.identifier.urihttps://doi.org/10.1109/ijcnn.2009.5178977
dc.identifier.urihttps://andeanlibrary.org/handle/123456789/50733
dc.language.isoen
dc.sourceInstitute for Computer Science and Control
dc.subjectCellular automaton
dc.subjectSeparable space
dc.subjectPolynomial
dc.subjectBinary number
dc.subjectComputer science
dc.subjectCellular neural network
dc.subjectMathematics
dc.subjectAutomaton
dc.subjectArtificial neural network
dc.subjectTheoretical computer science
dc.titleNew properties of 2D Cellular Automata found through Polynomial Cellular Neural Networks
dc.typearticle

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