title: Functional recognition imaging using artificial neural networks: applications to rapid cellular identification via broadband electromechanical response.
abstract: 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.
authors.affiliation: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN 37831, USA.
authors.forename: M P
authors.initials: MP
authors.lastname: Nikiforov
authors.forename: V V
authors.initials: VV
authors.lastname: Reukov
authors.forename: G L
authors.initials: GL
authors.lastname: Thompson
authors.forename: A A
authors.initials: AA
authors.lastname: Vertegel
authors.forename: S
authors.initials: S
authors.lastname: Guo
authors.forename: S V
authors.initials: SV
authors.lastname: Kalinin
authors.forename: S
authors.initials: S
authors.lastname: Jesse
mesh_terms: D018625:Microscopy, Atomic Force; D020527:Microscopy, Scanning Probe; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D025341:Principal Component Analysis