Differentiable optimization of the Debye-Wolf integral for light shaping and adaptive optics in two-photon microscopy.

Control of light through a microscope objective with a high numerical aperture is a common requirement in applications such as optogenetics, adaptive optics, or laser processing. Light propagation, including polarization effects, can be described under these conditions using the Debye-Wolf diffraction integral. Here, we take advantage of differentiable optimization and machine learning for efficiently optimizing the Debye-Wolf integral for such applications.

Differentiable model-based adaptive optics for two-photon microscopy.

Aberrations limit scanning fluorescence microscopy when imaging in scattering materials such as biological tissue. Model-based approaches for adaptive optics take advantage of a computational model of the optical setup. Such models can be combined with the optimization techniques of machine learning frameworks to find aberration corrections, as was demonstrated for focusing a laser beam through aberrations onto a camera [Opt.

Differentiable model-based adaptive optics with transmitted and reflected light.

Aberrations limit optical systems in many situations, for example when imaging in biological tissue. Machine learning offers novel ways to improve imaging under such conditions by learning inverse models of aberrations. Learning requires datasets that cover a wide range of possible aberrations, which however becomes limiting for more strongly scattering samples, and does not take advantage of prior information about the imaging process.

Wavefront correction for adaptive optics with reflected light and deep neural networks.

Light scattering and aberrations limit optical microscopy in biological tissue, which motivates the development of adaptive optics techniques. Here, we develop a method for wavefront correction in adaptive optics with reflected light and deep neural networks compatible with an epi-detection configuration. Large datasets of sample aberrations which consist of excitation and detection path aberrations as well as the corresponding reflected focus images are generated.

Virtual reality for animal navigation with camera-based optical flow tracking.

Background: Virtual reality combined with a spherical treadmill is used across species for studying neural circuits underlying navigation and learning.

New Method: We developed an optical flow-based method for tracking treadmill ball motion in real time using a single high-resolution camera.

Results: Tracking accuracy and timing were determined using calibration data.

Light scattering control in transmission and reflection with neural networks.

Scattering often limits the controlled delivery of light in applications such as biomedical imaging, optogenetics, optical trapping, and fiber-optic communication or imaging. Such scattering can be controlled by appropriately shaping the light wavefront entering the material. Here, we develop a machine-learning approach for light control.