## 1 Introduction
It is important to consider this point as some of these parameters evolve during development or can be pharmacologically controlled, hence causing effects on SACs [21]. It is known that stage II retinal waves are ubiquitous but some studies have found large variations in the bursting period of SACs across species [30]. [PERSON] et al. proposed that SACs burst because at stage II, they are close to a bifurcation (saddle node) and are triggered by small variations in membrane potential (noise or action of other cells), causing the cell to shift from a resting state to fast oscillations. Following this bifurcation, they remain in the rapid repetitive firing regime, activating the calcium-dependent slow potassium channels and generating a slow hyperpolarization current (SHP) that leads the cell to a homoclinic bifurcation, returning to its resting state and allowing for a long period of hyperpolarization [20]. The authors identified fast voltage-gated potassium channels as the rapid channels responsible for their high activation origin and the fast closure kinetics for rupture, and identified them as Kv3-type K\({}^{+}\) channel subunits. The voltage-dependent Kv3 channels play an essential role in this recognition process that reduces the neuron's refractory period [30; 31]. The presynaptic function of Kv3 channels remains to be investigated. Potassium conductance variation of the Kv3 channels acts on the transient intrinsic properties of SAC excitability during development. [PERSON] et al. proposed that this bifurcation influences the interval between SAC bursts [20]. They observed a wide range of interburst periods between species, which can be explained by the high variability or by an external current. Thus, in cholinergically coupled SACs, the rupture period exhibits intrinsic variability due to simple bifurcation arguments without the need for external mechanisms. A total of 637 genes responsible for visual impairment or even blindness have been described in the literature and are related to inherited and age-related retinal degenerations. In these situations, computational modeling of OR regarding retinal degeneration allows us to understand how these genetic dysfunctions cause process failures, understand their quantitative interdependencies, and discover the pathways that lead to cell death [32]. Regarding vision and eye diseases, there are several in vivo studies available in the literature (including those exploring various imaging modalities and functional assessment), comprehensive genotype-phenotype studies, and histopathological and anatomical studies. Due to the complex interrelationships and limitations of in vitro retinal systems, this theme is not entirely clear and computer modeling thus offers a way to address these challenges [33; 34]. The underlying principles of retinal cell biology are very similar to those of other tissues; therefore, this data can be exploited in reusable computational structures. However, it is known that the retina has a special structure involving a laminar organization and high metabolic demands. These constraints reduce the solutions of computer simulations, making this structure attractive to whole-cell and tissue models. Thus, vision-forming retinas have been highly studied by utilizing a set of knowledge and resources in the field of computational biomodeling [33]. Available computational models relate biophysical precision and hardware-adaptable implementation methodologies [35]. Obtaining clinical notes is an expensive process; therefore, the possibility of generating synthetic medical visual data is quite attractive and has been explored. However, for today's computer vision methods, the realistic generation of high-quality medical images is still a challenge [36; 37]. The initial methods for medical imaging were based on simplified mathematical models of human anatomy, and over time, they evolved into more complex models that involved the most important aspects of acquisition devices.