Provenance of Lyfe: Chemical Autonomous Agents Surviving through Associative Learning
arXiv:2210.05227 · doi:10.1103/PhysRevE.106.034401
Abstract
We present a benchmark study of autonomous, chemical agents exhibiting associative learning of an environmental feature. Associative learning has 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. The ability to encode environmental correlations and use them to make 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 behaviours in the biosphere, we aimed to explore whether simple, non-living 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 life-like behaviour, 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 realisation of these learning behaviours in protocell systems could advance a novel research direction in astrobiology, since our system significantly reduces the lower bound on the required complexity for emergent learning.
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