Achieving nanoscale precision using neuromorphic localization microscopy

Neuromorphic cameras are a new class of dynamic-vision-inspired sensors that encode the rate of change of intensity as events. They can asynchronously record intensity changes as spikes, independent of the other pixels in the receptive field, resulting in sparse measurements. This recording of such...

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Bibliographic Details
Published in:	Nature nanotechnology Vol. 18; no. 4; pp. 380 - 389
Main Authors:	Mangalwedhekar, Rohit, Singh, Nivedita, Thakur, Chetan Singh, Seelamantula, Chandra Sekhar, Jose, Mini, Nair, Deepak
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 01-04-2023 Nature Publishing Group
Subjects:	639/925/927/1007 639/925/930/328/2238 Cameras Centroids Chemistry and Materials Science Deep learning Diffusion Evaluation Fluorescence Image segmentation Localization Materials Science Nanotechnology Nanotechnology and Microengineering Neuromorphic computing Particle trajectories Receptive field Stochasticity Temporal resolution Trajectory analysis
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Summary:	Neuromorphic cameras are a new class of dynamic-vision-inspired sensors that encode the rate of change of intensity as events. They can asynchronously record intensity changes as spikes, independent of the other pixels in the receptive field, resulting in sparse measurements. This recording of such sparse events makes them ideal for imaging dynamic processes, such as the stochastic emission of isolated single molecules. Here we show the application of neuromorphic detection to localize nanoscale fluorescent objects below the diffraction limit, with a precision below 20 nm. We demonstrate a combination of neuromorphic detection with segmentation and deep learning approaches to localize and track fluorescent particles below 50 nm with millisecond temporal resolution. Furthermore, we show that combining information from events resulting from the rate of change of intensities improves the classical limit of centroid estimation of single fluorescent objects by nearly a factor of two. Additionally, we validate that using post-processed data from the neuromorphic detector at defined windows of temporal integration allows a better evaluation of the fractalized diffusion of single particle trajectories. Our observations and analysis is useful for event sensing by nonlinear neuromorphic devices to ameliorate real-time particle localization approaches at the nanoscale. A neuromorphic camera can localize single fluorescent particles to below 20 nm resolution and evaluate the diffusion trajectory with millisecond temporal precision.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1748-3387 1748-3395
DOI:	10.1038/s41565-022-01291-1