Nora Brackbill

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 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.

Generalized linear models fit macaque parasol ganglion cells under white noise but fail on naturalistic stimuli, even when fitted on them, and added scalar nonlinearities do not rescue them.