Optical Oscilloscope: Real-time, High-throughput, Volumetric Voltage Imaging

Subthreshold voltage conveys information about neuronal connectivity, network and neuromodulatory states critical to understanding brain circuit function and dysfunction.

Unlike spiking which can be monitored by calcium imaging and electrode arrays, no existing methods track subthreshold voltage fluctuations at network scale.

Multiphoton scanning modalities used to mitigate brain tissue scattering collect fluorescence too slowly to resolve small, fast voltage signals at network scale. Our goal is to develop network-scale, volumetric voltage imaging capability based on light-field microscopy (LFM).

LFM enables scanless, light-efficient volume acquisition, but its degradation by light scattering and computational complexity hinder its adaptation for network-scale voltage imaging.

We will overcome these limitations through a new generation of optics-aware, computationally efficient deep neural networks (DNNs) that will extract neuronal voltage signals from scattering-corrupted light fields. We will train the DNNs with kilohertz-rate light-field videos and scattering-robust scanned two-photon volumes.

We will implement the DNNs in a field-programmable gate array for real-time voltage readout to enable closed-loop experiments.

Our Optical Oscilloscope bridges the gap between existing techniques and the need for network-scale voltage imaging in neuroscience research.

Keywords: membrane voltage, neuroimaging, light-field microscopy, 3D imaging, real-time image processing, neural circuits, neural dynamics, machine learning, interpretable deep learning