  Challenges in the field of retinal prostheses motivate the development of
retinal models to accurately simulate Retinal Ganglion Cells (RGCs) responses.
The goal of retinal prostheses is to enable blind individuals to solve complex,
reallife visual tasks. In this paper, we introduce the functional assessment
(FA) of retinal models, which describes the concept of evaluating the
performance of retinal models on visual understanding tasks. We present a
machine learning method for FA: we feed traditional machine learning
classifiers with RGC responses generated by retinal models, to solve object and
digit recognition tasks (CIFAR-10, MNIST, Fashion MNIST, Imagenette). We
examined critical FA aspects, including how the performance of FA depends on
the task, how to optimally feed RGC responses to the classifiers and how the
number of output neurons correlates with the model's accuracy. To increase the
number of output neurons, we manipulated input images - by splitting and then
feeding them to the retinal model and we found that image splitting does not
significantly improve the model's accuracy. We also show that differences in
the structure of datasets result in largely divergent performance of the
retinal model (MNIST and Fashion MNIST exceeded 80% accuracy, while CIFAR-10
and Imagenette achieved ~40%). Furthermore, retinal models which perform better
in standard evaluation, i.e. more accurately predict RGC response, perform
better in FA as well. However, unlike standard evaluation, FA results can be
straightforwardly interpreted in the context of comparing the quality of visual
perception.
