E. J. Chichilnisky

The paper measures retinal resistivity using electrical resistance tomography in rat retinas, comparing results from commercial and machine learning-based methods.

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

This study develops a framework for cellular-resolution electrical stimulation of neurons using current steering and biophysical models, demonstrating its effectiveness in targeting retinal ganglion cells.

This paper introduces a compression framework for neural recording systems that reduces bandwidth demands while preserving spike detection accuracy.

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 develops a biophysical model to study retinal ganglion cell responses to multi-electrode stimulation and validates it with experimental data from macaque retinas.

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.

This paper introduces a dynamically optimized electrical stimulation method to enhance neural activity control in retinal implants, showing improved visual stimulus reconstruction compared to static methods.

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

This paper develops a nonlinear decoding method to infer natural images from primate retinal ganglion cell activity, showing improved accuracy over linear methods for high-pass features.

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.

The paper introduces a neural recording system combining microwire arrays with CMOS chips to enable high-density, three-dimensional neural interfaces.

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.

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.

This study examines the temporal resolution of primate rod photoreceptor responses to single photons and identifies key noise sources limiting their ability to encode photon arrival timing.

This paper presents a computational framework to simulate visual perception with a retinal prosthesis and assesses how learning might improve inferred visual perception.

The paper evaluates local return stimulation to improve selective activation of retinal ganglion cells at cellular resolution in isolated primate retina.

The paper examines ON and OFF retinal ganglion cell pathways in rats, finding pathway-specific asymmetries in receptive field properties.

Linear decoding followed by a convolutional autoencoder reconstructs natural images from 5398 simulated macaque retinal ganglion cell responses better than linear decoding (test mean squared error 0.0051 vs 0.0077).