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Provenance of life: Chemical autonomous agents surviving through associative learning

Bartlett, Stuart and Louapre, David (2022) Provenance of life: Chemical autonomous agents surviving through associative learning. Physical Review E, 106 (3). Art. No. .034401. ISSN 2470-0045. doi:10.1103/physreve.106.034401.

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We present a benchmark study of autonomous, chemical agents exhibiting associative learning of an environmental feature. Associative learning systems have been widely studied in cognitive science and artificial intelligence but are most commonly implemented in highly complex or carefully engineered systems, such as animal brains, artificial neural networks, DNA computing systems, and gene regulatory networks, among others. The ability to encode environmental information and use it to make simple predictions is a benchmark of biological resilience and underpins a plethora of adaptive responses in the living hierarchy, spanning prey animal species anticipating the arrival of predators to epigenetic systems in microorganisms learning environmental correlations. Given the ubiquitous and essential presence of learning behaviors in the biosphere, we aimed to explore whether simple, nonliving dissipative structures could also exhibit associative learning. Inspired by previous modeling of associative learning in chemical networks, we simulated simple systems composed of long- and short-term memory chemical species that could encode the presence or absence of temporal correlations between two external species. The ability to learn this association was implemented in Gray-Scott reaction-diffusion spots, emergent chemical patterns that exhibit self-replication and homeostasis. With the novel ability of associative learning, we demonstrate that simple chemical patterns can exhibit a broad repertoire of lifelike behavior, paving the way for in vitro studies of autonomous chemical learning systems, with potential relevance to artificial life, origins of life, and systems chemistry. The experimental realization of these learning behaviors in protocell or coacervate systems could advance a new research direction in astrobiology, since our system significantly reduces the lower bound on the required complexity for autonomous chemical learning.

Item Type:Article
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Bartlett, Stuart0000-0001-5680-476X
Louapre, David0000-0001-5068-1535
Additional Information:We gratefully acknowledge Artemy Kolchinsky of the University of Tokyo and Sante Fe Institute for his insightful and constructive review of a prepublication version of this manuscript. This work was supported by the Caltech Division of Geological and Planetary Sciences Discovery Fund. We thank the Caltech GPS "Astrobiothermoinfoevo" group for the numerous inspiring discussions that helped catalyze this work. Similarly, we thank the various members of the Earth-Life Science Institute and EON program members for creating such an inspiring and creative environment for intellectual exploration.
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Caltech Division of Geological and Planetary Sciences Discovery FundUNSPECIFIED
Issue or Number:3
Record Number:CaltechAUTHORS:20221017-12147700.14
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:117454
Deposited By: Research Services Depository
Deposited On:18 Oct 2022 22:02
Last Modified:18 Oct 2022 22:02

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