The “molecular” traveling salesman
Biological Cybernetics
An introduction to genetic algorithms
An introduction to genetic algorithms
Neural networks: a systematic introduction
Neural networks: a systematic introduction
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Artificial chemistries—a review
Artificial Life
Theoretical and Experimental DNA Computation (Natural Computing Series)
Theoretical and Experimental DNA Computation (Natural Computing Series)
A robot that walks; emergent behaviors from a carefully evolved network
Neural Computation
Genetic algorithms and artificial life
Artificial Life
Modeling adaptive autonomous agents
Artificial Life
UPP'04 Proceedings of the 2004 international conference on Unconventional Programming Paradigms
Review: Stochastic approaches for modelling in vivo reactions
Computational Biology and Chemistry
Fundamenta Informaticae
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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.