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Logical computation with self-assembling electric circuits

Inspired by self-assembled biological growth, the Circuit Tile Assembly Model (cTAM) was developed to provide insights into signal propagation, information processing, and computation in bioelectric networks. The cTAM is an abstract model that produces a family of circuits of different sizes that is...

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Detalles Bibliográficos
Autores principales: Yasmin, Rojoba, Deaton, Russell
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9728908/
https://www.ncbi.nlm.nih.gov/pubmed/36477295
http://dx.doi.org/10.1371/journal.pone.0278033
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author Yasmin, Rojoba
Deaton, Russell
author_facet Yasmin, Rojoba
Deaton, Russell
author_sort Yasmin, Rojoba
collection PubMed
description Inspired by self-assembled biological growth, the Circuit Tile Assembly Model (cTAM) was developed to provide insights into signal propagation, information processing, and computation in bioelectric networks. The cTAM is an abstract model that produces a family of circuits of different sizes that is amenable to exact analysis. Here, the cTAM is extended to the Boolean Circuit Tile Assembly Model (bcTAM) that implements a computationally complete set of Boolean gates through self-assembled and self-controlled growth. The proposed model approximates axonal growth in neural networks and thus, investigates the computational capability of dynamic biological networks, for example, in growing networks of axons. Thus, the bcTAM models the effect of electrical activity on growth and shows how that growth might implement Boolean computations. In this sense, given a set of input voltages, the bcTAM is a system that is able to monitor and make decisions about its own growth.
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spelling pubmed-97289082022-12-08 Logical computation with self-assembling electric circuits Yasmin, Rojoba Deaton, Russell PLoS One Research Article Inspired by self-assembled biological growth, the Circuit Tile Assembly Model (cTAM) was developed to provide insights into signal propagation, information processing, and computation in bioelectric networks. The cTAM is an abstract model that produces a family of circuits of different sizes that is amenable to exact analysis. Here, the cTAM is extended to the Boolean Circuit Tile Assembly Model (bcTAM) that implements a computationally complete set of Boolean gates through self-assembled and self-controlled growth. The proposed model approximates axonal growth in neural networks and thus, investigates the computational capability of dynamic biological networks, for example, in growing networks of axons. Thus, the bcTAM models the effect of electrical activity on growth and shows how that growth might implement Boolean computations. In this sense, given a set of input voltages, the bcTAM is a system that is able to monitor and make decisions about its own growth. Public Library of Science 2022-12-07 /pmc/articles/PMC9728908/ /pubmed/36477295 http://dx.doi.org/10.1371/journal.pone.0278033 Text en © 2022 Yasmin, Deaton https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Yasmin, Rojoba
Deaton, Russell
Logical computation with self-assembling electric circuits
title Logical computation with self-assembling electric circuits
title_full Logical computation with self-assembling electric circuits
title_fullStr Logical computation with self-assembling electric circuits
title_full_unstemmed Logical computation with self-assembling electric circuits
title_short Logical computation with self-assembling electric circuits
title_sort logical computation with self-assembling electric circuits
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9728908/
https://www.ncbi.nlm.nih.gov/pubmed/36477295
http://dx.doi.org/10.1371/journal.pone.0278033
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