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Experimental Demonstration of Hopfield Neural Network using DNA molecules

Published online by Cambridge University Press:  23 June 2011

Hayri E. Akin ,
Dundar Karabay ,
Allen P. Mills Jr. ,
Cengiz S. Ozkan  and
Mihrimah Ozkan
Hayri E. Akin
Affiliation:
Department of Electrical Engineering, University of California Riverside, Riverside, CA 92521, USA
Dundar Karabay
Affiliation:
Department of Physics and Astronomy, University of California Riverside, Riverside, CA 92521, USA
Allen P. Mills Jr.
Affiliation:
Department of Physics and Astronomy, University of California Riverside, Riverside, CA 92521, USA
Cengiz S. Ozkan
Affiliation:
Department of Mechanical Engineering, University of California Riverside, Riverside, CA 92521, USA
Mihrimah Ozkan
Affiliation:
Department of Electrical Engineering, University of California Riverside, Riverside, CA 92521, USA
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Abstract

DNA Computing is a rapidly-developing interdisciplinary area which could benefit from more experimental results to solve problems with the current biological tools. In this study, we have integrated microelectronics and molecular biology techniques for showing the feasibility of Hopfield Neural Network using DNA molecules. Adleman’s seminal paper in 1994 showed that DNA strands using specific molecular reactions can be used to solve the Hamiltonian Path Problem. This accomplishment opened the way for possibilities of massively parallel processing power, remarkable energy efficiency and compact data storage ability with DNA. However, in various studies, small departures from the ideal selectivity of DNA hybridization lead to significant undesired pairings of strands and that leads to difficulties in schemes for implementing large Boolean functions using DNA. Therefore, these error prone reactions in the Boolean architecture of the first DNA computers will benefit from fault tolerance or error correction methods and these methods would be essential for large scale applications. In this study, we demonstrate the operation of six dimensional Hopfield associative memory storing various memories as an archetype fault tolerant neural network implemented using DNA molecular reactions. The response of the network suggests that the protocols could be scaled to a network of significantly larger dimensions. In addition the results are read on a Silicon CMOS platform exploiting the semiconductor processing knowledge for fast and accurate hybridization rates.

Keywords

biologicalbiological synthesis (chemical reaction)fluidics
Type
Research Article
Information
MRS Online Proceedings Library (OPL) , Volume 1346: Symposium AA – Micro- and Nanofluidic Systems for Materials Synthesis, Device Assembly and Bioanalysis , 2011 , mrss11-1346-aa05-02
Copyright
Copyright © Materials Research Society 2011

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References

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