  The current biochemical information processing systems behave in a
predetermined 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.
