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Scholarly articles referencing pulse2percept

  • 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.
  • 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.