Towards Programming Adaptive Linear Neural Networks Through Chemical Reaction Networks
Dynamical Systems
2022-04-14 v2
Abstract
This paper is concerned with programming adaptive linear neural networks (ALNNs) using chemical reaction networks (CRNs) equipped with mass-action kinetics. Through individually programming the forward propagation and the backpropagation of ALNNs, and also utilizing the permeation walls technique, we construct a powerful CRN possessing the function of ALNNs, especially having the function of automatic computation. We also provide theoretical analysis and a case study to support our construction. The results will have potential implications for the developments of synthetic biology, molecular computer and artificial intelligence.
Keywords
Cite
@article{arxiv.2204.03168,
title = {Towards Programming Adaptive Linear Neural Networks Through Chemical Reaction Networks},
author = {Yuzhen Fan and Xiaoyu Zhang and Chuanhou Gao},
journal= {arXiv preprint arXiv:2204.03168},
year = {2022}
}