Performance limits and trade-offs in entropy-driven biochemical computers

TL;DR

This paper introduces entropy driven computers (EDCs) as a general model for chemical computation, revealing fundamental limits and trade-offs, especially between accuracy and entropy production, with implications for biological systems.

Contribution

It proposes the EDC model as a unifying framework for chemical computation and analyzes its fundamental trade-offs, including a novel insight that time does not impose a trade-off in entropy-driven computation.

Findings

Entropy driven computation involves a trade-off between accuracy and entropy production.

Observation of EDC states incurs an energy cost, affecting system performance.

Biological computers can be modeled as EDCs, including neural networks.

Abstract

The properties and fundamental limits of chemical computers have recently attracted significant interest as a model of computation, an unifying principle of cellular organisation and in the context of bio-engineering. As of yet, research in this topic is based on case-studies. There exists no generally accepted criterion to distinguish between chemical processes that compute and those that do not. Here, the concept of entropy driven computer (EDC) is proposed as a general model of chemical computation. It is found that entropy driven computation is subject to a trade-off between accuracy and entropy production, but unlike many biological systems, there are no trade-offs involving time. The latter only arise when it is taken into account that the observation of the state of the EDC is not energy neutral, but comes at a cost. The significance of this conclusion in relation to biological systems is discussed. Three examples of biological computers, including an implementation of a neural network as an EDC are given.