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Towards a Biomolecular Learning Machine

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7445)

Abstract

Learning and generalisation are fundamental behavioural traits of intelligent life. We present a synthetic biochemical circuit which can exhibit non-trivial learning and generalisation behaviours, which is a first step towards demonstrating that these behaviours may be realised at the molecular level. The aim of our system is to learn positive real-valued weights for a real-valued linear function of positive inputs. Mathematically, this can be viewed as solving a non-negative least-squares regression problem. Our design is based on deoxyribozymes, which are catalytic DNA strands. We present simulation results which demonstrate that the system can converge towards a desired set of weights after a number of training instances are provided.

Keywords

  • Logic Gate
  • Training Sequence
  • Training Instance
  • Substrate Molecule
  • Computational Element

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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© 2012 Springer-Verlag Berlin Heidelberg

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Lakin, M.R., Minnich, A., Lane, T., Stefanovic, D. (2012). Towards a Biomolecular Learning Machine. In: Durand-Lose, J., Jonoska, N. (eds) Unconventional Computation and Natural Computation. UCNC 2012. Lecture Notes in Computer Science, vol 7445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32894-7_15

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  • DOI: https://doi.org/10.1007/978-3-642-32894-7_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32893-0

  • Online ISBN: 978-3-642-32894-7

  • eBook Packages: Computer ScienceComputer Science (R0)