# Programming molecular systems to emulate a learning spiking neuron

arXiv: https://arxiv.org/abs/2206.02519 · HTML: https://mlsanity.com/paper/2206.02519/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 55 references that are in the map.
- First posted on arXiv 9 May 2022.

## Builds on (most cited first, 4 of 4)

- [Going Deeper in Spiking Neural Networks: VGG and Residual Architectures](https://mlsanity.com/paper/1802.02627/) (2018)
- [Event-based Feature Extraction Using Adaptive Selection Thresholds](https://mlsanity.com/paper/1907.07853/) (2019)
- [Minimal spiking neuron for solving multi-label classification tasks](https://mlsanity.com/paper/2003.02902/) (2020)
- [A domain-level DNA strand displacement reaction enumerator allowing arbitrary non-pseudoknotted secondary structures](https://mlsanity.com/paper/1505.03738/) (2015)

## Built on by (most cited first, 1 of 1)

- [STL-based Optimization of Biomolecular Neural Networks for Regression and Control](https://mlsanity.com/paper/2509.05481/) (2025)

## Abstract (arXiv)

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.