Autonomous Learning of Generative Models with Chemical Reaction Network Ensembles
This work addresses the challenge of autonomous learning in molecular systems, offering a foundational approach with potential applications in synthetic biology and nanotechnology.
The paper tackles the problem of enabling chemical systems to autonomously learn complex environmental distributions by developing a chemical implementation of gradient descent on relative entropy, showing it can optimize detailed balanced reaction networks and use hidden units for learning.
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 be applied to optimize any detailed balanced chemical reaction network and that the construction is capable of using hidden units to learn complex distributions. This result is then recast as a form of integral feedback control. Finally, due to our use of an explicit physical model of learning, we are able to derive thermodynamic costs and trade-offs associated to this process.