We present DeepVIDv2, a resolution-improved self-supervised voltage imaging denoising approach that achieves higher spatial resolution while preserving fast neuronal dynamics. While existing methods enhance signal-to-noise ratio (SNR), they compromise spatial resolution and result in blurry outputs. By disentangling spatial and temporal performance into two parameters, DeepVIDv2 overcomes the tradeoff faced by its predecessor. This advancement enables more effective analysis of high-speed, large-population voltage imaging data.
KEYWORDS: In vivo imaging, Two photon imaging, Neurons, Microscopes, Imaging systems, Ultrafast phenomena, High speed imaging, Fluorescence spectroscopy, Denoising
Monitoring spiking activity across large neuronal populations at behaviorally relevant timescales is critical for understanding neural circuit function. Voltage imaging requires kilohertz sampling rates which reduce fluorescence detection to near shot noise levels. High-photon flux excitation can overcome photon-limited shot noise but photo-bleaching and photo-damage restrict the number and duration of simultaneously imaged neurons. We investigated an alternative approach aimed at low two-photon flux, voltage imaging below the shot noise limit with the goal of achieving simultaneous high-speed, deep-tissue imaging of more than one hundred densely labeled neurons over one hour in awake behaving mice.
High-speed low-light two-photon voltage imaging is an emerging tool to simultaneously monitor neuronal activity from a large number of neurons. However, shot noise dominates pixel-wise measurements and the neuronal signals are difficult to be identified in the single-frame raw measurement. We developed a self-supervised deep learning framework for voltage imaging denoising, DeepVID, without the need for any high-SNR measurements. DeepVID infers the underlying fluorescence signal based on independent temporal and spatial statistics of the measurement that is attributable to shot noise. DeepVID achieved a 15-fold improvement in SNR when comparing denoised and raw image data.
We have designed and built a two-photon microscope which allows calcium imaging in awake, behaving animals across field-of-views (FOV) of up to 1.7 × 1.7 mm. A special scan system enables independent x,y, and z-positioning of two smaller sub-areas within this FOV for simultaneous functional recordings. This microscope enables us to optically record neuronal activity with cellular resolution across much larger spatial scales than previously possible and should help in deciphering the behavior-dependent flow of information within the neocortex. The microscope hard- and software are modular and can be extended to other imaging and photostimulation modalities.
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