Eric G. Wu

The paper decomposes retinal ganglion cell electrical images to infer cell types and functional properties using a novel optimization-based algorithm.

This study shows fixational eye movements improve retinal signal precision by enhancing spike timing and cell interactions in macaque retinas.

The paper trains a convolutional neural network to predict retinal ganglion cell responses to natural images in macaques and humans, finding it more accurate than a linear-nonlinear model.

The paper infers light responses of primate retinal ganglion cells using electrical features, achieving moderate correlation with measured firing rates.

The paper presents a method for fabricating 3D microelectrode arrays to interface with neural structures, demonstrating high-resolution recording from retinal ganglion cells.

This study demonstrates that high-fidelity electrical stimulation of human retinal ganglion cells is achievable with extracellular stimulation, validated through ex vivo experiments and comparison to macaque retina results.

The paper develops a method to reconstruct natural images from retinal ganglion cell activity using deep denoiser priors, achieving high perceptual similarity with fewer parameters than previous approaches.

The paper compares functional properties of major retinal ganglion cell types between humans and macaques using similar experimental methods, finding close parallels in their responses and organization.