5 78 5. Towards biologically plausible phosphene simulation for the localization of visual stimuli (Reuschel et al., 2012), and in cortical prostheses the position of the artificially induced percept will shift along with eye movements (Brindley & Lewin, 1968; Schmidt et al., 1996). Therefore, in prostheses with a head-mounted camera, misalignment between the camera orientation and the pupillary axes can induce localization problems (Caspi et al., 2018; Paraskevoudi & Pezaris, 2019; Sabbah et al., 2014; Schmidt et al., 1996). Previous SPV studies have demonstrated that eye-tracking can be implemented to simulate the gaze-coupled perception of phosphenes (Chaetal., 1992a; Dagnelie et al., 2006; McIntosh et al., 2013; Paraskevoudi & Pezaris, 2021; Rassia & Pezaris, 2018; Sommerhalder et al., 2004; Srivastava et al., 2009; Titchener et al., 2018). Note that some of the cited studies implemented a simulation condition where not only the simulated phosphene locations, but also the stimulation protocol depended on the gaze direction. More specifically, instead of representing the head-centered camera input, the stimulation pattern was chosen to encode the external environment at the location where the gaze was directed. While further research is required, there is some preliminary evidence that such a gaze-contingent image processing can improve the functional and subjective quality of prosthetic vision (Caspi et al., 2018; Paraskevoudi & Pezaris, 2021; Rassia & Pezaris, 2018; Titchener et al., 2018). Some example videos of gaze-contingent simulated prosthetic vision can be retrieved from our repository3. Note that an eye-tracker will be required to produce gaze-contingent image processing in visual prostheses and there might be unforeseen complexities in the clinical implementation thereof. The study of oculomotor behavior in blind individuals (with or without a visual prosthesis) is still an ongoing line of research (Caspi et al., 2018; Hafed et al., 2016; Kwon et al., 2013; Sabbah et al., 2014). Complexity and realism of the simulation There are some remaining challenges regarding the realistic simulation of the effects of neural stimulation. A complicating factor is that cortical neuroprostheses are still in the early stages of development. Neurostimulation hardware and stimulation protocols are continuously being improved (Beauchamp et al., 2020), and clinical trials with cortical visual neuroprostheses are often limited to small numbers of blind volunteers (Fernández & Normann, 2017; Troyk, 2017). Therefore, it is no surprise that the amount of data that is available at the present moment is limited, often open for multiple interpretations, and sometimes contains apparent contradictory information. Notably, the trade-off between model complexity and accurate psychophysical fits or predictions is a recurrent theme in the verification and validation of the components implemented in our simulator. Our approach aims to comprehensively integrate a set of biologically plausible models, while striking a balance between real-time performance, flexibility and biological realism. Combining models of current spread and knowledge about the retinotopic organization of the visual cortex with psychophysics allows us to link the space of electrical stimulation parameters with clinical perceptual reports as well as physiological knowledge in the NHP and clinical literature. These design choices play a role in some of the potential limitations of our current simulator. Here we name a few of the important limitations and some interesting directions for future research. Firstly, in our simulator, phosphenes are only rendered when the activation is above threshold. This might be an inaccurate depiction 3https://github.com/neuralcodinglab/dynaphos/blob/main/examples
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