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Scholarly articles referencing pulse2percept

  • A Xu, N Han, S Srivastava, D Klein, M Beyeler (2021). Enhancing simulated prosthetic vision with deep learning-based scene simplification strategies. Journal of Vision, doi:10.1167/jov.21.9.2308.
  • J Wang, H Zhu, J Liu, H Li, Y Han, R Zhou, Y Zhang (2021). The application of computer vision to visual prosthesis. Artificial Organs, doi:10.1111/aor.14022.
  • RB Esquenazi, K Meier, M Beyeler, GM Boynton, I Fine (2021). Learning to see again: Perceptual learning of simulated abnormal on- off-cell population responses in sighted individuals. Journal of Vision, doi:10.1167/jov.21.13.10.
  • L Wang, N Marek, J Steffen, S Pollmann (2021). Perceptual Learning of Object Recognition in Simulated Retinal Implant Perception - The Effect of Video Training. Translational Vision Science & Technology, doi:10.1167/tvst.10.12.22.
  • A Benetatos, N Melanitis, KS Nikita (2021). Assessing Vision Quality in Retinal Prosthesis Implantees through Deep Learning: Current Progress and Improvements by Optimizing Hardware Design Parameters and Rehabilitation. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, doi:10.1109/EMBC46164.2021.9630963.
  • N Han, S Srivastava, A Xu, D Klein, M Beyeler (2021). Deep Learning–Based Scene Simplification for Bionic Vision. arXiv preprint, arXiv:2102.00297.
  • D Haji Ghaffari (2021). Improving the Resolution of Prosthetic Vision through Stimulus Parameter Optimization. Dissertations and Theses (Ph.D. and Master’s), University of Michigan. doi:10.7302/3015
  • T Fauvel, M Chalk (2021). Human-in-the-loop optimization of visual prosthetic stimulation. bioRxiv doi:10.1101/2021.11.24.469867.
  • A Lozano, JS Suarez, C Soto-Sanchez, J Garrigos, JJ Martinez-Alvarez, JM Ferrandez, E Fernandez (2020). Neurolight: A Deep Learning Neural Interface for Cortical Visual Prostheses. International Journal of Neural Systems, doi:10.1142/S0129065720500458.
  • C Erickson-Davis, H Korzybska (2020). What do blind people “see” with retinal prostheses? Observations and qualitative reports of epiretinal implant users. bioRxiv, doi:10.1101/2020.02.03.932905.
  • E Migliorini (2019). A real-time, GPU-based VR solution for simulating the POLYRETINA retinal neuroprosthesis. Politecnico di Milano hdl.handle.net/10589/149850.
  • M Beyeler, GM Boynton, I Fine, A Rokem (2019). Model-Based Recommendations for Optimal Surgical Placement of Epiretinal Implants. Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, doi:10.1007/978-3-030-32254-0_44.
  • J Steffen, G Hille, K Tonnies (2019). Automatic Perception Enhancement for Simulated Retinal Implants. n Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2019), doi:10.5220/0007695409080914.
  • BW Brunton, M Beyeler (2019). Data-driven models in human neuroscience and neuroengineering. Current Opinion in Neurobiology 58, 21-29, doi:10.1016/j.conb.2019.06.008.
  • A Lozano, JS Suarez, C Soto-Sanchez, J Garrigos, J-J Martinez, JM Ferrandez Vicente, E Fernandez-Jover (2019). Neurolight Alpha: Interfacing Computational Neural Models for Stimulus Modulation in Cortical Visual Neuroprostheses. International Work-Conference on the Interplay Between Natural and Artificial Computation (IWINAC), doi:10.1007/978-3-030-19591-5_12.
  • M Beyeler (2019). Biophysical model of axonal stimulation in epiretinal visual prostheses. IEEE/EMBS Conference on Neural Engineering, doi:10.1109/NER.2019.8716969.
  • NP Cottaris, H Jiang, X Ding, BA Wandell, DH Brainard (2019). A computational-observer model of spatial contrast sensitivity: Effects of wave-front-based optics, cone-mosaic structure, and inference engine. Journal of Vision 19(8), doi:10.1167/19.4.8.
  • M Beyeler, D Nanduri, JD Weiland, A Rokem, GM Boynton, I Fine (2019). A model of ganglion axon pathways accounts for percepts elicited by retinal implants. Scientific Reports 9(1):9199, doi:10.1038/s41598-019-45416-4.
  • L Wang, F Sharifian, J Napp, C Nath, S Pollmann (2018). Cross-task perceptual learning of object recognition in simulated retinal implant perception. Journal of Vision 18(22), doi:10.1167/18.13.22.
  • J Huth, T Masquelier, A Arleo (2018). Convis: A toolbox to fit and simulate filter-based models of early visual processing. Frontiers in Neuroinformatics, doi:10.3389/fninf.2018.00009.
  • J Steffen, J Napp, S Pollmann, K Tönnies (2018). Perception Enhancement for Bionic Vision - Preliminary Study on Object Classification with Subretinal Implants. Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods, 169-177. doi:10.5220/0006648901690177.
  • JR Golden, C Erickson-Davis, NP Cottaris, N Parthasarathy, F Rieke, DH Brainard, BA Wandell, EJ Chichilnisky (2018): Simulation of visual perception and learning with a retinal prosthesis. bioRxiv 206409, doi:10.1101/206409.