  The generation of tailored light with multi-core fiber (MCF) lensless
microendoscopes is widely used in biomedicine. However, the computer-generated
holograms (CGHs) used for such applications are typically generated by
iterative algorithms, which demand high computation effort, limiting advanced
applications like in vivo optogenetic stimulation and fiber-optic cell
manipulation. The random and discrete distribution of the fiber cores induces
strong spatial aliasing to the CGHs, hence, an approach that can rapidly
generate tailored CGHs for MCFs is highly demanded. We demonstrate a novel
phase encoder deep neural network (CoreNet), which can generate accurate
tailored CGHs for MCFs at a near video-rate. Simulations show that CoreNet can
speed up the computation time by two magnitudes and increase the fidelity of
the generated light field compared to the conventional CGH techniques. For the
first time, real-time generated tailored CGHs are on-the-fly loaded to the
phase-only SLM for dynamic light fields generation through the MCF
microendoscope in experiments. This paves the avenue for real-time cell
rotation and several further applications that require real-time high-fidelity
light delivery in biomedicine.
