Learning Two-Input Linear and Nonlinear Analog Functions with a Simple Chemical System

  • Peter BandaEmail author
  • Christof Teuscher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8553)


The current biochemical information processing systems behave in a pre-determined manner because all features are defined during the design phase. To make such unconventional computing systems reusable and programmable for biomedical applications, adaptation, learning, and self-modification based on external stimuli would be highly desirable. However, so far, it has been too challenging to implement these in wet chemistries. In this paper we extend the chemical perceptron, a model previously proposed by the authors, to function as an analog instead of a binary system. The new analog asymmetric signal perceptron learns through feedback and supports Michaelis-Menten kinetics. The results show that our perceptron is able to learn linear and nonlinear (quadratic) functions of two inputs. To the best of our knowledge, it is the first simulated chemical system capable of doing so. The small number of species and reactions and their simplicity allows for a mapping to an actual wet implementation using DNA-strand displacement or deoxyribozymes. Our results are an important step toward actual biochemical systems that can learn and adapt.


Chemical perceptron analog perceptron supervised learning chemical computing RNMSE linear function quadratic function 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer SciencePortland State UniversityBroadwayUSA
  2. 2.Department of Electrical and Computer EngineeringPortland State UniversityBroadwayUSA

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