## 3 Computational Modeling of the Retina
Thus, the use of these pairs allows for a mapping of a binary vessel tree to a retinal image. For this propose, they used a recent technique of image-to-image translation based on the idea of contradictory learning. The results showed that while the original and generated images shared the same pot tree, they were visually different in overall appearance. Quantitative analysis of the quality of retinal synthetic images has further confirmed that the images produced retained a high proportion of the true quality of the image set [37]. To improve rehabilitation techniques, it is important to include the properties of neural degeneration in these computational approaches [88]. Stimulation thresholds have been well documented in clinical trials involving patients with severe retinal degeneration. It was concluded that the location of the electrode (epi- or subretinal) and the severity of neural degeneration affect the response resulting from electrical stimulation in the pacing range and threshold [101]. In vivo studies have observed increases in stimulation thresholds for indirect, presynaptic activation to retinal ganglion cells but the retina remains responsive if ganglion cells are directly stimulated [102; 103; 104; 105; 106]. In a study evaluating the electrophysiological findings of degenerate retinas, it was found that it is possible to alter the spontaneous activity in retinal ganglion cells through pharmacological inhibiting synapses [107]. In clinical trials, [PERSON] et al. demonstrated that the use of an enhanced pulse duration stimulates the internal neural circuitry to achieve a higher level of focus on the resulting light response [108]. Computational models represent the death of photoreceptors by considering direct stimulation of ganglion cells or indirect stimulation of ganglion cells through stimulation of presynaptic circuits [88; 109; 110]. Nevertheless, it must be kept in mind that photoreceptor death does not mean the end of the disease since it occurs early and has electrophysiological effects beyond photoreceptors, and light-induced entry into the retinal circuits does not occur. With the progression of the disease, there is a possibility that patients will be completely blind, and therefore there is extensive retinal remodeling, where cell migration and spontaneous neural activity are observed [111; 112]. During degeneration remodeling, spontaneous firing and oscillatory behavior were observed in the surviving neural networks. Therefore, removing the entry of light-sensitive cells does not remove neural activity in the retina; in fact, it becomes altered [113; 114]. The lack of presynaptic entry of degenerate photoreceptors leads to an oscillatory membrane voltage in the cells and thus leads to phasic disruption in the ganglion cells. Some studies have shown that blocking gap junctions can eliminate this activity [115; 116]. Previous studies have shown that there are changes in the response to electrical stimulation through these alterations in ganglion cell physiology in degenerating retina and spontaneous activity rates, which can disrupt attempts at systematic electrical stimulation to restore vision [117; 118; 119]. Given this information, [PERSON] et al. proposed a computational model of electrical retinal stimulation to investigate the waveforms used in prosthetic devices to restore lost vision [88]. In this model, there is an association with a connection-based neural network model, which is characterized by precise morphological and synaptic properties, with an admittance method model of bulk tissue and prosthetic electronics. The retina degeneration was modeled by considering cell death and anatomical changes that occur at the onset of the disease, as well as the altered neural behavior that develops throughout neurodegeneration.