Published In

Parallel Processing Letters

Document Type

Post-Print

Publication Date

3-2015

Subjects

Computational neuroscience, Perceptrons -- Analysis, Biochemical phenomena -- Experiments, Biomathematics, Neural networks (Computer science)

Abstract

Chemistry as an unconventional computing medium presently lacks a systematic approach to gather, store, and sort data over time. To build more complicated systems in chemistries, the ability to look at data in the past would be a valuable tool to perform complex calculations. In this paper we present the first implementation of a chemical delay line providing information storage in a chemistry that can reliably capture information over an extended period of time. The delay line is capable of parallel operations in a single instruction, multiple data (SIMD) fashion.

Using Michaelis-Menten kinetics, we describe the chemical delay line implementation featuring an enzyme acting as a means to reduce copy errors. We also discuss how information is randomly accessible from any element on the delay line. Our work shows how the chemical delay line retains and provides a value from a previous cycle. The system's modularity allows for integration with existing chemical systems. We exemplify the delay line capabilities by integration with a threshold asymmetric signal perceptron to demonstrate how it learns all 14 linearly separable binary functions over a size two sliding window. The delay line has applications in biomedical diagnosis and treatment, such as smart drug delivery.

Description

Authors' postprint of an article accepted for publication in Parallel Processing Letters, published by World Scientific.

DOI

10.1142/S0129626415400022

Persistent Identifier

http://archives.pdx.edu/ds/psu/12179

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