Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks

Kevin M. Cherry and Lulu Qian ()
Additional contact information
Kevin M. Cherry: Bioengineering, California Institute of Technology
Lulu Qian: Bioengineering, California Institute of Technology

Nature, 2018, vol. 559, issue 7714, 370-376

Abstract: Abstract From bacteria following simple chemical gradients1 to the brain distinguishing complex odour information2, the ability to recognize molecular patterns is essential for biological organisms. This type of information-processing function has been implemented using DNA-based neural networks3, but has been limited to the recognition of a set of no more than four patterns, each composed of four distinct DNA molecules. Winner-take-all computation4 has been suggested5,6 as a potential strategy for enhancing the capability of DNA-based neural networks. Compared to the linear-threshold circuits7 and Hopfield networks8 used previously3, winner-take-all circuits are computationally more powerful4, allow simpler molecular implementation and are not constrained by the number of patterns and their complexity, so both a large number of simple patterns and a small number of complex patterns can be recognized. Here we report a systematic implementation of winner-take-all neural networks based on DNA-strand-displacement9,10 reactions. We use a previously developed seesaw DNA gate motif3,11,12, extended to include a simple and robust component that facilitates the cooperative hybridization13 that is involved in the process of selecting a ‘winner’. We show that with this extended seesaw motif DNA-based neural networks can classify patterns into up to nine categories. Each of these patterns consists of 20 distinct DNA molecules chosen from the set of 100 that represents the 100 bits in 10 × 10 patterns, with the 20 DNA molecules selected tracing one of the handwritten digits ‘1’ to ‘9’. The network successfully classified test patterns with up to 30 of the 100 bits flipped relative to the digit patterns ‘remembered’ during training, suggesting that molecular circuits can robustly accomplish the sophisticated task of classifying highly complex and noisy information on the basis of similarity to a memory.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://www.nature.com/articles/s41586-018-0289-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:559:y:2018:i:7714:d:10.1038_s41586-018-0289-6

Ordering information: This journal article can be ordered from
https://www.nature.com/

DOI: 10.1038/s41586-018-0289-6

Access Statistics for this article

Nature is currently edited by Magdalena Skipper

More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().