Alexandra Kling

This study uses electrical stimulation to replicate natural spiking activity in primate retinal neurons, achieving lower variability than visual stimuli.

The paper examines how different primate retinal ganglion cell types respond to natural scenes, finding stereotyped response kinetics and distinct firing pattern repertoires across cell types.

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

The paper identifies functional diversity in primate retinal ganglion cells (RGCs) through multi-electrode recordings, revealing non-classical receptive field structures and specialized roles beyond spatial filtering.

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 infers electrical stimulation sensitivity of primate retinal ganglion cells from their recorded activity, showing that electrode features and cell compartmental differences affect stimulation thresholds.

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 evaluates the effectiveness of focal electrical stimulation in the central macaque retina to reproduce visual signals, finding higher expected image quality compared to the peripheral retina.

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 identifies three retinal mechanisms underlying saccadic suppression in mice, pigs, and macaques using electrophysiology and modeling, showing suppression arises from temporal filtering and nonlinearities rather than inhibition.

The paper examines individual variability in neural computations of the primate retina using machine learning and finds sex-based differences in firing rates and temporal integration.

The paper reconstructs natural images from primate retinal ganglion cell responses, showing that optimal filters reflect image statistics and include contributions from nearby cells.

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.

YASS sorts spikes from multi-electrode arrays with > 500 electrodes using neural network detection and denoising, parallelized nonparametric Bayesian clustering, and matching-pursuit deconvolution, outperforming manual sorting on primate retina recordings.

The paper introduces a method to infer nonlinear spatial subunits in primate retina using spike-triggered clustering, showing improved response prediction with subunit models in both natural and controlled stimuli.

This study investigates the role of 2δ-3 in mouse retinal ganglion cells, finding subtle changes in visual responses under low and moderate light levels but normal optokinetic reflexes.

The paper investigates the unique physiological properties of ON and OFF smooth monostratified ganglion cells in primate retinas, revealing nonlinear subunit properties and dendritic contributions to their receptive field structure.