Programming molecular systems to emulate a learning spiking neuron

TL;DR

This paper introduces a novel chemical reaction network capable of autonomous Hebbian learning, emulating a spiking neuron, and demonstrates potential pathways for engineering biochemical systems with learning abilities.

Contribution

It presents the first CRN that exhibits Hebbian learning, including a thermodynamically plausible minimal model and an enzyme-driven extended version, advancing synthetic biological intelligence.

Findings

CRN can learn statistical biases of inputs

Extended enzyme-driven CRN demonstrates adaptable learning

DNA strand displacement system models neuronal dynamics

Abstract

Hebbian theory seeks to explain how the neurons in the brain adapt to stimuli, to enable learning. An interesting feature of Hebbian learning is that it is an unsupervised method and as such, does not require feedback, making it suitable in contexts where systems have to learn autonomously. This paper explores how molecular systems can be designed to show such proto-intelligent behaviours, and proposes the first chemical reaction network (CRN) that can exhibit autonomous Hebbian learning across arbitrarily many input channels. The system emulates a spiking neuron, and we demonstrate that it can learn statistical biases of incoming inputs. The basic CRN is a minimal, thermodynamically plausible set of micro-reversible chemical equations that can be analysed with respect to their energy requirements. However, to explore how such chemical systems might be engineered de novo, we also propose an extended version based on enzyme-driven compartmentalised reactions. Finally, we also show how a purely DNA system, built upon the paradigm of DNA strand displacement, can realise neuronal dynamics. Our analysis provides a compelling blueprint for exploring autonomous learning in biological settings, bringing us closer to realising real synthetic biological intelligence.