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Learning in ensembles of proteinoid microspheres

Proteinoids are thermal proteins which form microspheres in water in the presence of salt. Ensembles of proteinoid microspheres exhibit passive nonlinear electrical properties and active neuron-like spiking of electrical potential. We propose that various neuromorphic computing architectures can be...

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Detalles Bibliográficos
Autores principales: Mougkogiannis, Panagiotis, Adamatzky, Andrew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10565364/
https://www.ncbi.nlm.nih.gov/pubmed/37830018
http://dx.doi.org/10.1098/rsos.230936
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author Mougkogiannis, Panagiotis
Adamatzky, Andrew
author_facet Mougkogiannis, Panagiotis
Adamatzky, Andrew
author_sort Mougkogiannis, Panagiotis
collection PubMed
description Proteinoids are thermal proteins which form microspheres in water in the presence of salt. Ensembles of proteinoid microspheres exhibit passive nonlinear electrical properties and active neuron-like spiking of electrical potential. We propose that various neuromorphic computing architectures can be prototyped from the proteinoid microspheres. A key feature of a neuromorphic system is a learning. Through the use of optical and resistance measurements, we study mechanisms of learning in ensembles of proteinoid microspheres. We analyse 16 types of proteinoids study and their intrinsic morphology and electrical properties. We demonstrate that proteinoids can learn, memorize and habituate, making them a promising candidate for novel computing.
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spelling pubmed-105653642023-10-12 Learning in ensembles of proteinoid microspheres Mougkogiannis, Panagiotis Adamatzky, Andrew R Soc Open Sci Computer Science and Artificial Intelligence Proteinoids are thermal proteins which form microspheres in water in the presence of salt. Ensembles of proteinoid microspheres exhibit passive nonlinear electrical properties and active neuron-like spiking of electrical potential. We propose that various neuromorphic computing architectures can be prototyped from the proteinoid microspheres. A key feature of a neuromorphic system is a learning. Through the use of optical and resistance measurements, we study mechanisms of learning in ensembles of proteinoid microspheres. We analyse 16 types of proteinoids study and their intrinsic morphology and electrical properties. We demonstrate that proteinoids can learn, memorize and habituate, making them a promising candidate for novel computing. The Royal Society 2023-10-11 /pmc/articles/PMC10565364/ /pubmed/37830018 http://dx.doi.org/10.1098/rsos.230936 Text en © 2023 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited.
spellingShingle Computer Science and Artificial Intelligence
Mougkogiannis, Panagiotis
Adamatzky, Andrew
Learning in ensembles of proteinoid microspheres
title Learning in ensembles of proteinoid microspheres
title_full Learning in ensembles of proteinoid microspheres
title_fullStr Learning in ensembles of proteinoid microspheres
title_full_unstemmed Learning in ensembles of proteinoid microspheres
title_short Learning in ensembles of proteinoid microspheres
title_sort learning in ensembles of proteinoid microspheres
topic Computer Science and Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10565364/
https://www.ncbi.nlm.nih.gov/pubmed/37830018
http://dx.doi.org/10.1098/rsos.230936
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