Nanoscale artificial intelligence: creating artificial neural networks using autocatalytic reactions

TL;DR

This paper introduces a methodology for designing nanoscale systems of autocatalytic reactions that emulate artificial neural networks, enabling self-regulation of particle concentrations to produce specific outputs in response to inputs.

Contribution

It establishes a mathematical framework linking neural network parameters to reaction rates, enabling the engineering of robust nanoscale neural-like systems.

Findings

System can produce prescribed outputs despite disturbances

Mathematical equivalence between neural networks and autocatalytic reactions

Framework enables precise control of nanoscale reaction systems

Abstract

A general methodology is proposed to engineer a system of interacting components (particles) which is able to self-regulate their concentrations in order to produce any prescribed output in response to a particular input. The methodology is based on the mathematical equivalence between artificial neurons in neural networks and species in autocatalytic reactions, and it specifies the relationship between the artificial neural network's parameters and the rate coefficients of the reactions between particle species. Such systems are characterised by a high degree of robustness as they are able to reach the desired output despite disturbances and perturbations of the concentrations of the various species.