Abstract
Vision enables visual perception of one's environs, as well as self-navigation within space. Objects within our environs are visible by virtue of the fact that they re ect light. To see or have visual perception, this light needs to be converted into an electrical signal. This process is referred to as visual transduction and takes place in the retina. Recently, it has become apparent that the convergence of rod and cone systems in transduction is crucial to enable retina functionality. Specifically, for local adaptation and contrast gain control in response to changes in illumination. However, because research until recently showed that rod and cone pathways have operated autonomously of each other, existing retinal models and designs of retinal prosthesis have been of either one of these pathways and have not incorporated their convergence. In this thesis we introduce a new retina model, which is biologically plausible, computationally simple and effective, and one that captures the convergence of rod and cone pathways both in the Outer and Inner Plexiform Layers (O/I PL) of the retina. In the OPL, we introduce rod cone convergence via electrical gap junctions to simulate rod-cone coupling. We demonstrate that introducing convergence in the OPL improves the perception of input stimuli and extends the range of adaptation to light levels. In the IPL, we introduce the convergence by developing a simulated rod On Bipolar Cell (ONBC) and introducing it via a rod pathway into the cone system via an Amacrine model. At this layer, we were able to show improved visual acuity as well as an increase in the dynamic range by improving contrast enhancement at very high luminance levels. Our results are compared with biology to determine whether rod and cone convergence gives rise to a better model of biology as measured through the threshold versus intensity (tvi) function. We also assess the signal-to-noise ratio results of the model when compared with an image processing technique to determine if the model has computational benefits. The results obtained from our retinal model show that if incorporated in the design of retinal prosthesis and visual systems used in robotics, there should be marked improvement during visual processing.