Presentation Type
Poster
Start Date
5-8-2013 11:00 AM
Subjects
Perceptrons, Artificial intelligence, Neurons -- Mathematical models
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 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.
Rights
© Copyright the author(s)
IN COPYRIGHT:
http://rightsstatements.org/vocab/InC/1.0/
This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
DISCLAIMER:
The purpose of this statement is to help the public understand how this Item may be used. When there is a (non-standard) License or contract that governs re-use of the associated Item, this statement only summarizes the effects of some of its terms. It is not a License, and should not be used to license your Work. To license your own Work, use a License offered at https://creativecommons.org/
Persistent Identifier
http://archives.pdx.edu/ds/psu/9483
Included in
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.