Training an asymmetric signal perceptron through reinforcement in an artificial chemistry.
State-of-the-art biochemical systems for medical applications and chemical computing are application-specific and cannot be reprogrammed or trained once fabricated. The implementation of adaptive biochemical systems that would offer flexibility through programmability and autonomous adaptation faces major challenges because of the large number of required chemical species as well as the timing-sensitive feedback loops required for learning. In this paper, we begin addressing these challenges with a novel chemical perceptron that can solve all 14 linearly separable logic functions. The system performs asymmetric chemical arithmetic, learns through reinforcement and supports both Michaelis-Menten as well as mass-action kinetics. To enable cascading of the chemical perceptrons, we introduce thresholds that amplify the outputs. The simplicity of our model makes an actual wet implementation, in particular by DNA-strand displacement, possible.
Department of Computer Science, Portland State University, , PO Box 751, Portland, OR 97207, USA.
Peter
P
Banda
Christof
C
Teuscher
Darko
D
Stefanovic
D004247:DNA / Q000737:chemistry*; D008956:Models, Chemical
D004247:DNA
chemical perceptron; mass-action kinetics; reinforcement learning; representation asymmetry; robustness; thresholding