Start Date

8-5-2013 11:00 AM

Subjects

Perceptrons, Artificial intelligence, Neurons -- Mathematical models

Description

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 poster, I present a simulated chemical system, the first full-featured implementation of a perceptron in an artificial (simulated) chemistry, which can successfully learn all 14 linearly separable logic functions. A perceptron is the simplest system capable of learning inspired by the functioning of a biological neuron. My newest model called the asymmetric signal perceptron (ASP) is, as opposed to its predecessors such as the weight-race perceptron (WRP), substantially simpler by exploiting asymmetric chemical arithmetics and is fully described by mass-action kinetics. I suggest that DNA strand displacement could, in principle, provide an implementation substrate for my model, allowing the chemical perceptron to perform reusable, programmable and adaptable wet biochemical computing.

Persistent Identifier

http://archives.pdx.edu/ds/psu/9483

Included in

Chemistry Commons

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May 8th, 11:00 AM

Training an Asymmetric Signal Perceptron in an Artificial Chemistry

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 poster, I present a simulated chemical system, the first full-featured implementation of a perceptron in an artificial (simulated) chemistry, which can successfully learn all 14 linearly separable logic functions. A perceptron is the simplest system capable of learning inspired by the functioning of a biological neuron. My newest model called the asymmetric signal perceptron (ASP) is, as opposed to its predecessors such as the weight-race perceptron (WRP), substantially simpler by exploiting asymmetric chemical arithmetics and is fully described by mass-action kinetics. I suggest that DNA strand displacement could, in principle, provide an implementation substrate for my model, allowing the chemical perceptron to perform reusable, programmable and adaptable wet biochemical computing.