Reference : Learning Two-input Linear and Nonlinear Analog Functions with a Simple Chemical System
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Physical, chemical, mathematical & earth Sciences : Chemistry
Engineering, computing & technology : Computer science
Computational Sciences
Learning Two-input Linear and Nonlinear Analog Functions with a Simple Chemical System
Banda, Peter mailto [Portland State University > Department of Computer Science]
Teuscher, Christof [> >]
Unconventional Computing and Natural Computing Conference
Ibarra, Oscar H.
Kari, Lila
Kopecki, Steffen
Springer International Publishing
Lecture Notes in Computer Science, 8553
The 13th International Conference on Unconventional Computation and Natural Computation
from 14-07-2014 to 18-07-2014
[en] chemical perceptron ; analog perceptron ; supervised learning ; chemical computing ; RNMSE ; linear function ; quadratic function
[en] 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.

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1404.0427v2.pdfarXiv versionAuthor preprint403.75 kBView/Open

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