Abstract
Spiking networks, the third generation of neural networks, are presented as low-power consumption machines with higher cognitive ability, one of the main concerns in intelligence machines. In fact, neuromorphic systems are hardware implementations of spiking networks with minimum resource, area, and power consumption while preserve maximum working frequency. Here, the focus is on the digital implementation of Retinal Ganglion Cell (RGC) based on the linear approximation of non-linear terms which is called Linear Retinal Ganglion Cell (LRGC). The low-cost hardware design of biological cells is acceptable when the digital model of the cell has the same phase and time domain behavior as the original model and follows the dynamic behavior of the original model accurately, which is discussed and confirmed with different analyzes in this paper. The low-cost hardware design of biological cells allows the optimal implementation of a neural population on the hardware, provided that the collective behavior of the digital model matches the original model which is approved by the large-scale simulation of RGC and LRGC models. Cognitive processes are performed in the nervous system at a very low cost, which neuromorphic systems are trying to achieve this important. In this regard, the behavior of RGC and LRGC models in the reconstruction of the image through the retina pathway was examined and a high agreement between the performance of the two models was achieved. Finally, the high functional compatibility of RGC, LRGC models proves that the proposed model is a good candidate of the main model in neuromorphic systems with low hardware cost.