Autonomous Learning of Generative Models with Chemical Reaction Network Ensembles. (arXiv:2311.00975v1 [q-bio.MN])

Can a micron sized sack of interacting molecules autonomously learn an
internal model of a complex and fluctuating environment? We draw insights from
control theory, machine learning theory, chemical reaction network theory, and
statistical physics to develop a general architecture whereby a broad class of
chemical systems can autonomously learn complex distributions. Our construction
takes the form of a chemical implementation of machine learning's optimization
workhorse: gradient descent on the relative entropy cost function. We show how
this method can …

architecture arxiv autonomous bio control environment general generative generative models insights learn machine machine learning micron molecules network physics statistical systems theory