# DNA Reservoir Computing: A Novel Molecular Computing Approach

arXiv: https://arxiv.org/abs/1306.5998 · HTML: https://mlsanity.com/paper/1306.5998/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2013.

## Built on by (most cited first, 7 of 7)

- [A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning](https://mlsanity.com/paper/2307.15092/) (2023)
- [An introduction to reservoir computing](https://mlsanity.com/paper/2412.13212/) (2024)
- [Circuit design in biology and machine learning. I. Random networks and dimensional reduction](https://mlsanity.com/paper/2408.09604/) (2024)
- [Heterogeneous Reservoir Computing Models for Persian Speech Recognition](https://mlsanity.com/paper/2205.12594/) (2022)
- [Neural networks consisting of DNA](https://mlsanity.com/paper/2501.03235/) (2024)
- [Exoplanetary atmospheres retrieval via a quantum extreme learning machine](https://mlsanity.com/paper/2509.03617/) (2025)
- [Vestibular reservoir computing](https://mlsanity.com/paper/2604.09943/) (2026)

## Abstract (arXiv)

We propose a novel molecular computing approach based on reservoir computing. In reservoir computing, a dynamical core, called a reservoir, is perturbed with an external input signal while a readout layer maps the reservoir dynamics to a target output. Computation takes place as a transformation from the input space to a high-dimensional spatiotemporal feature space created by the transient dynamics of the reservoir. The readout layer then combines these features to produce the target output. We show that coupled deoxyribozyme oscillators can act as the reservoir. We show that despite using only three coupled oscillators, a molecular reservoir computer could achieve 90% accuracy on a benchmark temporal problem.