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dc.contributor.authorLindell, Trym A. E.
dc.contributor.authorHudcová, Barbora
dc.contributor.authorNichele, Stefano
dc.identifier.citationHiroyuki, I., Suzuki, K., Uno R., Damiano L., Spychala N., Aguilera M., Izquierdo E., Suzuki R. & Baltieri, M. (Red.). (2023). ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference. MIT Press.en_US
dc.description.abstractCellular Automata (CAs) have potential as powerful parallel computational systems, which has lead to the use of CAs as reservoirs in reservoir computing. However, why certain Cellular Automaton (CA) rules, sizes and input encodings are better or worse at a given task is not well understood. We present a method that enables identification and visualization of the specific information content, flow and transformations within the space-time diagram of CA. We interpret each spatio-temporal location in CA’s space-time diagram as a function of its input and call this novel notion the CA’s Canonical Computations (CCs). This allows us to analyze the available information from the space-time diagrams as partitions of the input set. The method also reveals how input-encoder-rule interactions transform the information flow by changing features like spatial and temporal location stability as well as the specific information produced. This general approach for analysing CA is discussed for the engineering of reservoir computing systems.en_US
dc.publisherMIT Pressen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.subjectKomplekse systemeren_US
dc.subjectComplex systemsen_US
dc.subjectArtificial Intelligenceen_US
dc.titleCanonical Computations in Cellular Automata and Their Application for Reservoir Computingen_US
dc.typePeer revieweden_US
dc.subject.nsiVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550en_US
dc.relation.projectNorges forskningsråd: 286558en_US

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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal