A model of ganglion axon pathways accounts for percepts elicited by retinal implants.

Degenerative retinal diseases such as retinitis pigmentosa and macular degeneration cause irreversible vision loss in more than 10 million people worldwide. Retinal prostheses, now implanted in over 250 patients worldwide, electrically stimulate surviving cells in order to evoke neuronal responses that are interpreted by the brain as visual percepts ('phosphenes'). However, instead of seeing focal spots of light, current implant users perceive highly distorted phosphenes that vary in shape both across subjects and electrodes. We characterized these distortions by asking users of the Argus retinal prosthesis system (Second Sight Medical Products Inc.) to draw electrically elicited percepts on a touchscreen. Using ophthalmic fundus imaging and computational modeling, we show that elicited percepts can be accurately predicted by the topographic organization of optic nerve fiber bundles in each subject's retina, successfully replicating visual percepts ranging from 'blobs' to oriented 'streaks' and 'wedges' depending on the retinal location of the stimulating electrode. This provides the first evidence that activation of passing axon fibers accounts for the rich repertoire of phosphene shape commonly reported in psychophysical experiments, which can severely distort the quality of the generated visual experience. Overall our findings argue for more detailed modeling of biological detail across neural engineering applications

PySilSub: An open-source Python toolbox for implementing the method of silent substitution in vision and nonvisual photoreception research

The normal human retina contains several classes of photosensitive cell—rods for low-light vision, three cone classes for daylight vision, and intrinsically photosensitive retinal ganglion cells (ipRGCs) expressing melanopsin for non-image-forming functions, including pupil control, melatonin suppression, and circadian photoentrainment. The spectral sensitivities of the photoreceptors overlap significantly, which means that most lights will stimulate all photoreceptors to varying degrees. The method of silent substitution is a powerful tool for stimulating individual photoreceptor classes selectively and has found much use in research and clinical settings. The main hardware requirement for silent substitution is a spectrally calibrated light stimulation system with at least as many primaries as there are photoreceptors under consideration. Device settings that will produce lights to selectively stimulate the photoreceptor(s) of interest can be found using a variety of analytic and algorithmic approaches. Here we present PySilSub (https://github.com/PySilentSubstitution/pysilsub), a novel Python package for silent substitution featuring flexible support for individual colorimetric observer models (including human and mouse observers), multiprimary stimulation devices, and solving silent substitution problems with linear algebra and constrained numerical optimization. The toolbox is registered with the Python Package Index and includes example data sets from various multiprimary systems. We hope that PySilSub will facilitate the application of silent substitution in research and clinical settings

Enhanced Memory for Scenes Presented at Behaviorally Relevant Points in Time

What determines whether a scene is remembered or forgotten? Our results show how visual scenes are encoded into memory at behaviorally relevant points in time

Visual Cortex: The Continuing Puzzle of Area V2

AbstractSurprisingly little is known about the role of V2 in visual processing. A recent study found that the responses of V2 neurons to pairs of angled lines could be predicted from their responses to the individual line components. A simple analysis shows how these neurons may simply sum the responses from one or more orientation selective V1 neurons