Reference : Online Learning in a Chemical Perceptron
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Chemistry
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/25882
Online Learning in a Chemical Perceptron
English
Banda, Peter mailto [Portland State University > Department of Computer Science]
Teuscher, Christof [> >]
Lakin, Matthew R. [> >]
2013
Artificial life
MIT Press
19
2
195-219
Yes
International
[en] perceptron ; artificial chemistry ; chemical reaction network ; learning ; adaptation ; robustness ; chemical computing
[en] 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.
http://hdl.handle.net/10993/25882
10.1162/ARTL_a_00105
http://www.mitpressjournals.org/doi/pdf/10.1162/ARTL_a_00105

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