## 4 Metadata Analysis
### Computational Model of Electrical Stimulation of the Retina  
[PERSON] et al. proposed a computer model of electrical retinal stimulation to investigate waveforms already used in prosthetic devices to restore partial vision lost by retinal degenerative diseases [41]. To understand the degenerate retinal response to electrical stimulation used and to develop new electrode geometries and stimulus waveforms, the authors provided a simulation framework for increasing the effectiveness of electrical stimulation. Based on previous work, the framework was developed as a multi-scale and multiphysics simulation platform where the Admittance Method is used to compute the electric field within a tissue model and NEURON is used to simulate the resulting response in a neural network [130]. The Admittance Method is assumed to be a quasi-static  
Figure 3: Schematic description of the pre-processing, retina model using Virtual Retina, and post-processing general operator. HDR: high dynamic range, RGB: Red, Green, Blue color model, OPL: outer plexiform layer, CGC: contrast gain control, LDR: low dynamic range. Modified after [PERSON] et al. [92]. electromagnetic field solver, where a voxelized model that is discretized by tissue/material dielectric properties is constructed first. It takes as data the input between each node in the model and a set of current sources, where a set of linear equations is constructed and an iterative solver is used to calculate the voltage at each node in the model [131]. Based on a connectivity dataset, a model of a ganglion cell network with a realistic cell morphology and synaptic type, distribution, and weight is built on NEURON [132]. In the study by [PERSON] et al. [41], the biophysical data available in the literature were incorporated into each cell and a script was also written to apply the extracellular electric field from Admittance Method simulations to observe the resulting network and cellular behavior at a given input. The intrinsic properties of AII amacrine cells within this model were altered (following [PERSON] et al.) to produce an oscillatory membrane potential and induce spontaneous neural activity that mimics a degenerated retina [116]. For this study, translating a connectome dataset into a computational model is a uniquely qualified approach since it is composed of a real image morphology and observed connections that allow for greater precision in the representation of a retinal neural network and the resulting network behavior, which reduces the need for assumptions about interconnectivity. Thus, it allows for the representation of cell death within the internal plexiform layer by \"degenerating\" the computational retina and decreasing the strength of individual synapses or gap junctions and the number of cells. A bulk tissue and prosthetic electronics model were constructed on a larger spatial scale using the Admittance Method to calculate the extracellular electric field due to anatomical changes in the early stages of degeneration. Connecting the extracellular space model of the Admittance Method model to the connectome-based NEURON model combines the ability to alter the anatomical structure of the retina, integrate implant electronics, and alter the biophysical properties of cells and interconnectivity to virtually degenerate the retina. The detailed study from [PERSON] et al. [41], of degenerated retinal electrical stimulation includes knowledge gained through electrophysiological studies of cellular behavior, the comprehensive maps of connections, geometric and electrical characteristics of prosthetic electrode arrays, and clinical and experimental data on degeneration, as well as computational electromagnetism, all in one simulation platform (Figure 4). In this study, the stimulation threshold and interval were calculated for varying degrees of degeneration and complexity of the neural network, and a stimulation waveform that reduces spontaneous activity within a degenerate retina is created, which provides more effective control over ganglion cell stimulation.