3D Micropatterned Multiphoton Stimulation via Deep Learning-Based Computer-Generated Holography with Temporal Focusing Confinement

The customized 3D illumination patterns can be generated with computer-generated holography (CGH), and the axial confinement of the illumination patterns can be improved by inducing the temporal focusing technique. Through these approaches, the neuron excitation in single-cell resolution can be achieved. However, due to the computation cost of iterative CGH algorithm, the hologram must be pre-calculated to generate the illumination patterns for neuron excitation. This shortcoming makes it difficult to dynamically stimulate the neurons for observing neural activity. To overcome this issue for real-time dynamic neuron stimulation, we develop a neuron stimulation system with single-cell resolution and a real-time CGH algorithm. For single-cell resolution, a diffraction grating is used to generate the temporal focusing effect. Moreover, we design a deep-learning based CGH algorithm considering temporal focusing effect to real-time generate hologram with the pre-trained U-net architecture for producing customized illumination patterns in 3D positions. In our approach, the dynamic 3D micro-patterned single-cell neural excitation can be achieved by inducing temporal focusing technique to improve the axial resolution to few microns level and generating hologram by deep-learning based CGH considering temporal focusing to speed up the computation time to tens of milliseconds.