Sein Kim, M.S.

1. Kim, S., Roh, H., and Im, M. (2022). Artificial visual information produced by retinal prostheses. Front Cell Neurosci. 16, 9111754. doi: 10.3389/fncel.2022.911754
2. Roh, H.*, Kim, S.*, Lee, H. M., and Im, M. (2024). Quantitative analyses of how optimally heterogeneous neural codes maximize neural information in jittery transmission environments. Scientific Reports 14. 29623. doi: 10.1038/s41598-024-81029-2
3. An, N. M., Roh, H., Kim, S., Kim, J. H., and Im, M. (2025). Machine learning techniques for simulating human psychophysical testing of low-resolution phosphene face images in artificial vision. Advanced Science . 2405789. doi: 10.1002/advs.202405789
4. Kim, D. E.*, Kim, S.*. Kim, M., Min, B. K., and Im, M. (2025) Retinal degeneration increases inter-trial variabilities of light-evoked spiking activities in ganglion cells. Exp Eye Res 253. 110305. doi: 10.1016/j.exer.2025.110305

International

1. Kim, S.*, Roh, H.*, and Im, M. (2022). “Neural information of artificial vision varies depending on mean firing rate and spiking duration”, The Association for Research in Vision and Ophthalmology (ARVO’22)
   
2. Kim, S., An, N. M., Roh, H., Kim, J. H., and Im, M. (2022). “Human subject gaze tracking and artificial neural network saliency maps during visual recognition of phosphene array face images for artificial vision”, The Korean Society for Brain and Neural Sciences (KSBNS’22), Incheon, Korea
3. An, N. M., Roh, H., Kim, S., Kim, J. H., and Im, M. (2023). “Reinforcement Learning Framework to Simulate Short-Term Learning Effects of Human Psychophysical Experiments Assessing the Quality of Artificial Vision”, International Joint Conference on Neural Network (IJCNN’ 23), Queensland, Australia 
4. Kim, D. E.*, Kim, S.*, Shim, S.*, Park, B., Jung, Y., Kim, J. H., and Im, M. (2023). “Low-cost multielectrode array system for color vision study using digital micromirror device projector”, The IEEE Engineering in Medicine and Biology Society (EMBC’23), Sydney, Australia
5. Kim, S., Kim, D. E., Shim, S., Park, B., Jung, Y., Kim, J. H., and Im, M. (2023). “Intercellular correlation levels of retinal ganglion cell spiking activities in responses to various natural images”, The IEEE Engineering in Medicine and Biology Society (EMBC’23), Sydney, Australia
6. Kim, S., and Im, M. (2023). “Both Spiking Heterogeneity and Neural Information Vary in the Primary Visual Cortex Depending on Stimulus Types”, The IEEE Engineering in Medicine and Biology Society (EMBC’23), Sydney, Australia 
7. Kim, D. E., Kim, S., Shim, S., Park, B., Kim, J. H., and Im, M. (2023). “Retinal Degeneration Increases Variability of Firing Pattern in Ganglion Cells to Light Stimulation”, The Korean Society for Brain and Neural Sciences (KSBNS’23), Busan, Korea
8. Im, M., Kim, D., Roh, H., Kim, S., Kim, D. E., Kim, M., Yi, D., Kim, S. Y., and Kwon, S. (2023). “Molecular alterations in the isoforms of HCN channels within the retina during the advancement of outer retinal degeneration in rd10 mice”, The Korean Society for Brain and Neural Sciences (KSBNS’23), Busan, Korea
9. Kim, S., Kim, D. E., Kim, M., and Im, M. †, “Comparative analysis of clustering methods with spiking activities for classification of retinal ganglion cell types,” The 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’24), Orlando, FL, USA, Jul. 15-19, 2024.
10. Kim, D. E., Kim, S., Min, B., and Im, M.†, “Retinal degeneration increases variabilities of light-evoked spiking activities in ganglion cells depending on stimulus contrast,” The 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’24), Orlando, FL, USA, Jul. 15-19, 2024.
    
11. Kim, Y., Kim, S., Roh, H., Kim, D. E., and Im, M.†, “Comparative Analysis of Spike-Time Correlation in Wild-Type and Optogenetically Treated Degenerated Retinas,” The 28th Annual Meeting of the Korean Society for Brain and Neural Sciences (KSBNS'25), Incheon, South Korea, Aug. 24-27, 2025.

Domestic

1. Shim, H., Kim, S., and Im, M. (2023). “An Efficient Way to Create Artificial Vision Phosphene Images using XAI”, The Korean Society of Medical & Biological Engineering, Seoul, Korea
2. Kim, S., Shim, H., Lee, H., and Im, M. (2024). “Performance Comparison Depending on Electrode Conditions of Artificial Retinal Prosthesis using Deep Learning Method”, The Korean Micro Electro Mechanical Systems Conference (KMEMS’ 24), Jeju, Korea