## 1 Introduction
Thus, the most realistic images are produced by combining anatomical and physiological medical knowledge [38]. These images have many applications, including validating image analysis techniques, medical training, and therapy planning, among others [36; 37; 39]. The traditional top-down approach is the means used to check available data and draw mathematical models that explain it via image simulation. This approach models complex natural laws through inevitable simplifying assumptions. However, there is a new view in the field of medical imaging, where a bottom-up approach is used to directly derive the most important information from that data. This new approach involves machine learning systems that automatically learn the internal variability of training data [40]. This system, based on the general idea of contradictory learning, is capable of synthesizing natural images, which led to an advance in computer vision. Hardware adaptative optics (HAO) has pushed the limits of optical imaging, which when combined with the optical coherence tomography associated with HAO, has enabled a detailed three-dimensional visualization of photoreceptor distributions and individual nerve fiber bundles in the living human retina [33]. Several models have been created to recreate retinal waves but none can reproduce the autonomous rhythmic disruption of individual SACs and explain the change in intrinsic properties of these cells during development [20]. Based on current vision restoration modes, algorithmic models exhibit a greater correlation between stimuli and the affected neural network; however, the practicality of physical hardware hinders development. By adapting current processing methods, it is possible to complement large-scale circuit design techniques, which allows for the better physical construction of the artificial retina [35]. To restore vision in patients with degenerative diseases, retinal prosthetic devices have been developed, which through electrical stimulation of the local regions of retinal tissue, bring together spatiotemporal patterns to represent the patient-facing image. These devices have proven to be effective, which has led to the design of various types of devices, many of which are in trials and some are already in the market [41]. For example, Argus II by Second Sight Medical Products Inc. (Sylmar, CA, USA) is already in clinical trials and has been shown to provide patients considerable visual perception to the extent that subjects can recognize objects and differentiate letters. Nevertheless, based on safety limits, 45% of individual electrodes failed to elicit light perception. Thus, adjacent electrodes are required to locally stimulate the cells [42]. These findings allow for a considerable increase in functional vision in blind individuals but new strategies to improve vision have yet to be studied [43; 44]. Some tests have already been done to decrease the magnitude of the current required or recreate the desired peak rates in retinal ganglion cells such that the efficiency and effectiveness of the stimulus waveform can be improved [45; 46; 47]. Whole-cell simulation is still recent, particularly in mammalian cells, and is therefore considered in biology as one of the challenges of the 21 st century. Whole-cell modeling emerged from developments in proteomics, structural biology, and cryoelectronic microscopy, as well as from trying to understand the dynamic behavior of cells and how the genotype expresses itself in the phenotype. Software development that can simulate whole-cells (E-Cell) or constraint-based genome-scale metabolic network modeling (COBRA toolbox) has been utilized in these fields [48; 49]. The characteristic anatomy and interaction of the cellular and extracellular compartments means that the manipulation and robust testing using in vitro cell models and OR computational models is crucial [50; 51; 52].