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Feasibility of a Hopfield Neural Network Using DNA Molecules

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Abstract:

Adleman's 1994 proof that DNA oligomers using specific molecular reactions can be used to solve the Hamiltonian Path Problem suggested the possibility of massively parallel processing power, remarkable energy efficiency and compact data storage ability for this new type of computation. The Boolean architecture of the first DNA computers and the fact that DNA hybridization reactions can be error prone indicates that some form of fault tolerance or error correction would be beneficial in any large scale applications. In this study, we demonstrate the operation of a four dimensional Hopfield associative memory storing two memories as an archetype fault tolerant neural network implemented using DNA molecular reactions. The response of the network compares favorably to a computer simulation and suggests that the protocols could be scaled to a network of significantly larger dimensions.

Keywords: DNA COMPUTATION; HOPFIELD; NEURAL NETWORK

Document Type: Research Article

DOI: https://doi.org/10.1166/jctn.2011.1865

Publication date: 2012-01-01

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  • Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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