Abstract

To perform real-time stimulation of neurons and simultaneous observation of the neural connectome, a deep learning-based computer-generated holography (DeepCGH) system has been developed. This system utilized a neural network to generate a hologram, which is then real-time projected onto a high refresh rate spatial light modulator (SLM) to generate fast 3D micropatterns. However, DeepCGH had two limitations: the computation time is increased as the number of input layers grew, and it cannot reconstruct arbitrary 3D micropatterns within the same model. To address these issues, integrated a digital propagation matrix (DPM) into the DeepCGH data preprocessing to generate arbitrary 3D micropatterns within the same model and reduce the computation time. Furthermore, to incorporate temporal focusing confinement (TFC), the axial resolution (FWHM) is improved from 30 μm to 6 μm, and then it can avoid to excite other cells. As a result, the DeepCGH with DPM system is able to timely generate customized micropatterns within a 150-μm volume with high accuracy. With DPM, the DeepCGH was able to generate arbitrary 3D micropatterns and further save 50% computation time. Additionally, the DeepCGH holograms achieve superior results in optical reconstruction and have high accuracy in both position and depth as combined with TFC.

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