Cargando…
Computational capability of ecological dynamics
Ecological dynamics is driven by complex ecological networks. Computational capabilities of artificial networks have been exploited for machine learning purposes, yet whether an ecological network possesses a computational capability and whether/how we can use it remain unclear. Here, we developed t...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10113807/ https://www.ncbi.nlm.nih.gov/pubmed/37090968 http://dx.doi.org/10.1098/rsos.221614 |
_version_ | 1785027911902822400 |
---|---|
author | Ushio, Masayuki Watanabe, Kazufumi Fukuda, Yasuhiro Tokudome, Yuji Nakajima, Kohei |
author_facet | Ushio, Masayuki Watanabe, Kazufumi Fukuda, Yasuhiro Tokudome, Yuji Nakajima, Kohei |
author_sort | Ushio, Masayuki |
collection | PubMed |
description | Ecological dynamics is driven by complex ecological networks. Computational capabilities of artificial networks have been exploited for machine learning purposes, yet whether an ecological network possesses a computational capability and whether/how we can use it remain unclear. Here, we developed two new computational/empirical frameworks based on reservoir computing and show that ecological dynamics can be used as a computational resource. In silico ecological reservoir computing (ERC) reconstructs ecological dynamics from empirical time series and uses simulated system responses for information processing, which can predict near future of chaotic dynamics and emulate nonlinear dynamics. The real-time ERC uses real population dynamics of a unicellular organism, Tetrahymena thermophila. The temperature of the medium is an input signal and population dynamics is used as a computational resource. Intriguingly, the real-time ecological reservoir has necessary conditions for computing (e.g. synchronized dynamics in response to the same input sequences) and can make near-future predictions of empirical time series, showing the first empirical evidence that population-level phenomenon is capable of real-time computations. Our finding that ecological dynamics possess computational capability poses new research questions for computational science and ecology: how can we efficiently use it and how is it actually used, evolved and maintained in an ecosystem? |
format | Online Article Text |
id | pubmed-10113807 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-101138072023-04-20 Computational capability of ecological dynamics Ushio, Masayuki Watanabe, Kazufumi Fukuda, Yasuhiro Tokudome, Yuji Nakajima, Kohei R Soc Open Sci Ecology, Conservation and Global Change Biology Ecological dynamics is driven by complex ecological networks. Computational capabilities of artificial networks have been exploited for machine learning purposes, yet whether an ecological network possesses a computational capability and whether/how we can use it remain unclear. Here, we developed two new computational/empirical frameworks based on reservoir computing and show that ecological dynamics can be used as a computational resource. In silico ecological reservoir computing (ERC) reconstructs ecological dynamics from empirical time series and uses simulated system responses for information processing, which can predict near future of chaotic dynamics and emulate nonlinear dynamics. The real-time ERC uses real population dynamics of a unicellular organism, Tetrahymena thermophila. The temperature of the medium is an input signal and population dynamics is used as a computational resource. Intriguingly, the real-time ecological reservoir has necessary conditions for computing (e.g. synchronized dynamics in response to the same input sequences) and can make near-future predictions of empirical time series, showing the first empirical evidence that population-level phenomenon is capable of real-time computations. Our finding that ecological dynamics possess computational capability poses new research questions for computational science and ecology: how can we efficiently use it and how is it actually used, evolved and maintained in an ecosystem? The Royal Society 2023-04-19 /pmc/articles/PMC10113807/ /pubmed/37090968 http://dx.doi.org/10.1098/rsos.221614 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 | Ecology, Conservation and Global Change Biology Ushio, Masayuki Watanabe, Kazufumi Fukuda, Yasuhiro Tokudome, Yuji Nakajima, Kohei Computational capability of ecological dynamics |
title | Computational capability of ecological dynamics |
title_full | Computational capability of ecological dynamics |
title_fullStr | Computational capability of ecological dynamics |
title_full_unstemmed | Computational capability of ecological dynamics |
title_short | Computational capability of ecological dynamics |
title_sort | computational capability of ecological dynamics |
topic | Ecology, Conservation and Global Change Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10113807/ https://www.ncbi.nlm.nih.gov/pubmed/37090968 http://dx.doi.org/10.1098/rsos.221614 |
work_keys_str_mv | AT ushiomasayuki computationalcapabilityofecologicaldynamics AT watanabekazufumi computationalcapabilityofecologicaldynamics AT fukudayasuhiro computationalcapabilityofecologicaldynamics AT tokudomeyuji computationalcapabilityofecologicaldynamics AT nakajimakohei computationalcapabilityofecologicaldynamics |