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Supervised Learning in an Adaptive DNA Strand Displacement Circuit

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 9211)

Abstract

The development of DNA circuits capable of adaptive behavior is a key goal in DNA computing, as such systems would have potential applications in long-term monitoring and control of biological and chemical systems. In this paper, we present a framework for adaptive DNA circuits using buffered strand displacement gates, and demonstrate that this framework can implement supervised learning of linear functions. This work highlights the potential of buffered strand displacement as a powerful architecture for implementing adaptive molecular systems.

Keywords

  • Strand Displacement Systems
  • Target Weight Values
  • Subcircuits
  • Learning Circuits
  • Training Round

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Acknowledgments

This material is based upon work supported by the National Science Foundation under grants 1318833 and 1422840. M.R.L. gratefully acknowledges support from the New Mexico Cancer Nanoscience and Microsystems Training Center.

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Correspondence to Matthew R. Lakin .

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Lakin, M.R., Stefanovic, D. (2015). Supervised Learning in an Adaptive DNA Strand Displacement Circuit. In: Phillips, A., Yin, P. (eds) DNA Computing and Molecular Programming. DNA 2015. Lecture Notes in Computer Science(), vol 9211. Springer, Cham. https://doi.org/10.1007/978-3-319-21999-8_10

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  • DOI: https://doi.org/10.1007/978-3-319-21999-8_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21998-1

  • Online ISBN: 978-3-319-21999-8

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