Online Learning in a Chemical Perceptron.
Artificial Life, 19 (2):
195--219 (2013)DOI: 10.1162/ARTL_a_00105
Abstract
Abstract Autonomous learning implemented purely by
means of a synthetic chemical system has not been
previously realized. Learning promotes reusability and
minimizes the system design to simple input-output
specification. In this article we introduce a chemical
perceptron, the first full-featured implementation of a
perceptron in an artificial (simulated) chemistry. A
perceptron is the simplest system capable of learning,
inspired by the functioning of a biological neuron. Our
artificial chemistry is deterministic and
discrete-time, and follows Michaelis-Menten kinetics.
We present two models, the weight-loop perceptron and
the weight-race perceptron, which represent two
possible strategies for a chemical implementation of
linear integration and threshold. Both chemical
perceptrons can successfully identify all 14 linearly
separable two-input logic functions and maintain high
robustness against rate-constant perturbations. We
suggest that DNA strand displacement could, in
principle, provide an implementation substrate for our
model, allowing the chemical perceptron to perform
reusable, programmable, and adaptable wet biochemical
computing.