2022/05/09 by Jakub Fil, Fil, Jakub, Neil Dalchau +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Neuroscience · Physics and Astronomy · #Advanced biosensing and bioanalysis techniques #Artificial Intelligence (cs.AI) #FOS: Biological sciences #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE) #Neurons and Cognition (q-bio.NC) #Photoreceptor and optogenetics research #Spectroscopy and Quantum Chemical Studies
paper · pdf · doi:10.48550/arxiv.2206.02519
openalex publication_date 2022/05/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.