KEYWORDS: Matrices, Computer generated holography, Digital holography, 3D modeling, Computation time, Spatial light modulators, Holograms, Data modeling, Temporal resolution, Neurons
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.
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.
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