## 3 Computational Modeling of the Retina
The model can adapt to the emergence of new data since the parameters and formulations are gathered from the publication of experimental and modeling studies [35]. [PERSON] et al. used a convolutional neural network (CNN) for total retinal segmentation in optical coherence tomography (OCT) to develop an automated system that is resistant to the presence of severe retinal pathology. To explain retinal changes, a generalized U-net network architecture was introduced in this model [89]. The algorithm proposed by the authors was shown to be qualitatively and quantitatively superior compared with the available algorithms because it calculated the macular thickness with an error smaller than the error obtained in the other algorithms. Thus, this study showed that the proposed algorithm can model a wide variability in retinal appearance, with robust and reliable retinal segmentation in any type of pathology. Real-world irradiation values involve a large range of magnitudes that need to be processed by bionic systems to capture a snapshot of the visual scenes with an exceptional optical sensitivity. To capture high dynamic range (HDR) images, cameras capable of capturing them are required, where most standard cameras implement the method proposed by [PERSON] and [PERSON] [90]. Because standard monitors have a low dynamic range (LDR), problems arise in the later viewing of images. Two solutions are proposed: the technical solution, where HDR monitors are used but these are not yet available to the public; and the algorithmic solution, where methods are used to reduce the intensity from HDR to LDR [91]. These methods are known as tone-mapping operators (TMOs) and are designed for still scenes and videos. Over time, there has been intense research on still images according to retinal principles, where the studied approaches combine brightness adjustment and local contrast improvement [92]. There has been a growing interest and development regarding video tone mapping [90]. In neuroscience, it has been shown that comparable TMOs take place at the retina through adjustment mechanisms, which are indispensable once the retina has to preserve contrast sensitivity over this extensive luminance spectrum in ordinary scenes [92]. [PERSON] et al. developed an innovative TMO based on the strength of an interactive model of the retina [92]. They departed from the Virtual Retina simulator (VRS) that was developed to model the main layers and cell types found in the primate retina. It is grounded in biology and it has been validated using neurophysiological data [93]. This model is considered to be a good example of synergistic model construction since it explores an indispensable interaction between neuroscience and computer vision [53]. It is also interesting for a TMO since it includes a non-trivial contrast gain control (CGC) mechanism, which improves the local contrast in images and even improves coherence in the temporal luminousness. However, VRS still cannot solve the tone-mapping task because it is not designed to handle color or HDR images since there is no photoreception adapter or reading mechanism to produce data regarding the multiple retinal responses supplied from different complex cell layers. In this approach, these issues enrich the VRS model, preserve its bio-plausibility, and tests it on standard tone mapping images [92]. [PERSON] et al. developed a TMO derived from the Retinex algorithm [94]. This yielded a restricted non-linear operator based upon the framework model by [PERSON] and [PERSON], whose study established the interdependence between the eye and brain in color processing, namely, the color constancy effect [95]. The proposed TMO developed by [PERSON] et al. using the Retinex algorithm processes each color channel independently, which is further unified with the color restoration method [94]. This method has two modes of operation: (1) Single scale, where each color channel is convolved into small Gaussian clusters of different sizes.