An Efficient Digital Realization of Retinal Light Adaptation in Cone Photoreceptors

In recent years, hardware modeling for various parts of the body's sensitive organs, including the brain and nervous system, heart and eyes, has been considered for the treatment of diseases and rehabilitation, as well as for moving towards the construction of artificial prostheses. The retina is a thin layer that is the innermost layer of the human eye. In this paper, low-cost hardware implementation for retinal cone cells is performed. Existing mathematical models for implementing the behavior of these cells include a series of nonlinear functions that, if implemented directly, would require a large amount of hardware and, in addition, would not have the desired speed. The proposed model uses multi-linear functions to approximate the nonlinear terms and eliminate the multiplication expressions. The simulation results show that the proposed model tracks the behavior of the original model with high precision. There is also a good match between the main model and the proposed model in terms of dynamic behaviors. The results of hardware implementation using the virtex5 XC5VLX20T (2FF323) reconfigurable board (FPGA) show that the proposed model is fully valid and has a lower hardware volume as well as a 4 times higher frequency, and 22% less power consumption than the original model.

Automated summary

This study focuses on low-cost hardware implementation for retinal cone cells. The proposed model uses multi-linear functions to approximate nonlinear terms and eliminate multiplication expressions, achieving high precision behavior tracking and good match in dynamic behaviors with the main model. Hardware implementation results show that the proposed model is fully valid, with lower hardware volume, 4 times higher frequency, and 22% less power consumption than the original model.