# Towards Programming Adaptive Linear Neural Networks Through Chemical Reaction Networks

Source: [Towards Programming Adaptive Linear Neural Networks Through Chemical Reaction Networks](<https://www.sota2.com/research/paper/68fa59271e209131ce9b44fe>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T15:43:24.976Z

Authors: Yuzhen Fan, Xiaoyu Zhang, Chuanhou Gao

Published: 2022-04-07T02:38:58.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2204.03168>)

[PDF](<https://arxiv.org/pdf/2204.03168.pdf>)