Nowadays, numerous studies have investigated the modeling of efficient neural encoding processes in the retina of the eye to encode the sensory data. Retina, as the innermost coat of the eye, is the first and the most important area of the visual neural system of mammalians, which is responsible for neural processes. Retina encodes the information of light intensity into a sequence of spikes, and sends them to retinal ganglion cells (RGCs) for further processing. An appropriate retinal encoding model should be adapted to the real retina as much as possible by considering the physiological constraints of the visual pathway to transfer most of the information of the input signal to the brain without too much redundancy of the channel. In this paper, inspired from the existing linear models of retinal encoding process, which have employed input noise and the spatial locality of the RGCs receptive fields (RFs) in the calculation of the encoding matrix, two extra physiological constraints, adapted from the real retina are taken into account so as to achieve a more realistic model for the mammalian retina. These new constraints that are the correlation between RGCs and the spatial locality of the photoreceptors’ projective fields (PFs), are modeled in a mathematical form and analyzed in detail. To quantify fidelity of the proposed encoding matrix and prove its superiority over existing models, various parameters of the models are calculated and presented in this paper: mean square error between the original and reconstructed image (MSE), the redundancy of the channel, the amount of information transferred through the channel, and the amount of wasted capacity for carrying input noise, to name a few. The results of these calculations show that the proposed model transfers input information with less redundancy of the channel. In other words, it reduces a portion of channel capacity which is wasted for carrying the input noise in comparison to the existing models. Also, due to considering extra physiological constraints in the proposed model, it is acceptable to have a slightly higher amount of MSE value in order to become similar to the real retina.

如今，许多研究已经研究了在眼睛视网膜中有效的神经编码过程的建模，以对感觉数据进行编码。视网膜是眼睛最里面的一层，是哺乳动物视觉神经系统的第一个也是最重要的区域，它负责神经过程。视网膜将光强度信息编码为一系列尖峰，然后将它们发送到视网膜神经节细胞（RGC）进行进一步处理。适当的视网膜编码模型应通过考虑视觉通路的生理约束条件，尽可能地适应实际视网膜，以将大部分输入信号信息传输到大脑，而不会过多地占用通道。本文从视网膜编码过程的现有线性模型中获得启发，在编码矩阵的计算中使用了输入噪声和RGC感受域（RF）的空间局部性的情况下，考虑了来自真实视网膜的两个额外的生理约束，从而为哺乳动物提供了一个更真实的模型视网膜。这些新的约束是RGC与感光器投射场（PF）的空间局部性之间的相关性，以数学形式建模并进行了详细分析。为了量化所提出的编码矩阵的保真度并证明其在现有模型上的优越性，本文计算并提出了模型的各种参数：原始图像和重建图像（MSE）之间的均方误差，通道的冗余度，数量通过渠道转移的信息，以及用于传输输入噪声的浪费容量的数量，仅举几例。这些计算的结果表明，所提出的模型以较少的信道冗余传输输入信息。换句话说，与现有模型相比，它减少了浪费在传输输入噪声上的信道容量的一部分。此外，由于在提出的模型中考虑了额外的生理限制，因此可以接受稍高一些的MSE值，以使其与真实视网膜相似。