Functional recognition imaging using artificial neural networks: applications to rapid cellular identification via broadband electromechanical response

Functional recognition imaging in scanning probe microscopy (SPM) using artificial neural network identification is demonstrated. This approach utilizes statistical analysis of complex SPM responses at a single spatial location to identify the target behavior, which is reminiscent of associative thi...

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Bibliographic Details
Published inNanotechnology Vol. 20; no. 40; p. 405708
Main Authors Nikiforov, M P, Reukov, V V, Thompson, G L, Vertegel, A A, Guo, S, Kalinin, S V, Jesse, S
Format Journal Article
LanguageEnglish
Published England IOP Publishing 07.10.2009
Subjects
Artificial neural networks
Bacteria
Broadband
Cellular
Imaging
Microscopy, Atomic Force
Microscopy, Scanning Probe
Models, Theoretical
Nanotechnology
Neural Networks (Computer)
Position (location)
Principal Component Analysis
Pseudomonas fluorescens
Recognition
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Summary:Functional recognition imaging in scanning probe microscopy (SPM) using artificial neural network identification is demonstrated. This approach utilizes statistical analysis of complex SPM responses at a single spatial location to identify the target behavior, which is reminiscent of associative thinking in the human brain, obviating the need for analytical models. We demonstrate, as an example of recognition imaging, rapid identification of cellular organisms using the difference in electromechanical activity over a broad frequency range. Single-pixel identification of model Micrococcus lysodeikticus and Pseudomonas fluorescens bacteria is achieved, demonstrating the viability of the method.
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ISSN:0957-4484
1361-6528
DOI:10.1088/0957-4484/20/40/405708