We present Neural CRNs, an efficient, autonomous, and general-purpose implementation of learning within mass action chemical reaction systems. Unlike prior works, which transliterate discrete neural networks into chemical systems, Neural CRNs are a purely analog chemical system, which encodes neural computations in the concentration dynamics of its chemical species. Consequently, the chemical reactions in this system stay true to their nature, behaving as atomic end-to-end computational units, resulting in concise and efficient reaction network implementations. We demonstrate this efficiency by assembling a highly streamlined supervised learning procedure that requires only two clock phases. We further validate the robustness of our framework by constructing Neural CRN circuits for several linear and nonlinear regression and classification tasks. Furthermore, a minimal linear regression circuit is assembled using only 13 reactions and 15 species. Our nonlinear modeling circuits significantly advance the state-of-the-art through compact and simple implementations. The synergistic nature of our framework with the analog chemical computing hardware leaves ample room for optimizations and approximations in the computational model, several of which are discussed in this work. Our work introduces a novel paradigm for chemical computing and learning, providing a foundational platform for future adaptive biochemical circuits with applications in fields such as synthetic biology, bioengineering, and adaptive biomedicine.
View on arXiv@article{nagipogu2025_2409.00034,
title={ Neural CRNs: A Natural Implementation of Learning in Chemical Reaction Networks },
author={ Rajiv Teja Nagipogu and John H. Reif },
journal={arXiv preprint arXiv:2409.00034},
year={ 2025 }
}