iv Contents 3.2 Methods .................................. 27 3.2.1 Thevirtualmobilitysetup . . . . . . . . . . . . . . . . . . . . . . 27 3.2.2 Optimizationprocedure. . . . . . . . . . . . . . . . . . . . . . . 30 3.2.3 Experiments............................. 31 3.3 Results ................................... 33 3.3.1 Baselineresults ........................... 33 3.3.2 Edgedetectionthresholds . . . . . . . . . . . . . . . . . . . . . . 34 3.3.3 Phospheneresolution........................ 34 3.4 Discussion ................................. 35 3.4.1 Primaryoutcomes.......................... 35 3.4.2 Generalconsiderations ....................... 36 3.4.3 Conclusion.............................. 37 4 End-to-end optimization of prosthetic vision 39 4.1 Introduction ................................ 40 4.2 Methods .................................. 41 4.2.1 Modeldescription.......................... 41 4.3 ExperimentsandResults.......................... 43 4.3.1 TrainingProcedure.......................... 44 4.3.2 Experiment1............................. 44 4.3.3 Experiment2............................. 45 4.3.4 Experiment3............................. 46 4.3.5 Experiment4............................. 48 4.4 Discussion ................................. 51 4.4.1 Automatedoptimization. . . . . . . . . . . . . . . . . . . . . . . 51 4.4.2 Tailored optimization to sparsity constraints. . . . . . . . . . . . . 52 4.4.3 Task-specific optimization for naturalistic settings. . . . . . . . . . 52 4.4.4 Tailored optimization to realistic phosphene mappings . . . . . . . 53 4.4.5 Limitations and future directions . . . . . . . . . . . . . . . . . . 54 4.5 Conclusion................................. 54 5 Towards biologically plausible phosphene simulation 57 5.1 Introduction ................................ 58 5.1.1 Backgroundandrelatedwork . . . . . . . . . . . . . . . . . . . . 59 5.2 Materialsandmethods........................... 62 5.2.1 Visuotopicmapping......................... 62 5.2.2 Phosphenesize ........................... 63 5.2.3 Phosphenebrightness........................ 64 5.2.4 Stimulationthreshold........................ 65 5.2.5 Temporaldynamics......................... 65 5.2.6 Parameterestimates......................... 65 5.3 Results ................................... 66 5.3.1 Biologicalplausibility........................ 66 5.3.2 Performance............................. 67 5.3.3 Usability in a deep learning SPV pipeline . . . . . . . . . . . . . . 67
RkJQdWJsaXNoZXIy MTk4NDMw