Helene M. Schreyer

The paper develops a spatial contrast model to capture nonlinear spatial integration in primate retinal ganglion cells using natural and artificial stimuli, showing improved performance over linear models.

This study examines how primate retinal ganglion cells adapt their spatial processing to natural versus artificial stimuli, finding that some cell types adjust receptive fields and surrounds, but efficient coding only partially explains these changes.

The paper compares filter-based models of suppression in retinal ganglion cells across species and stimuli, finding divisive models generalize better across temporal frequencies and contrasts, while subtractive models capture slow dynamics.

This study shows that natural stimuli with species-specific gaze patterns drive correlated spiking responses in retinal ganglion cells, violating efficient coding theory.

This paper introduces STR, a method combining super-resolution and tomography to identify receptive field subunits in retinal ganglion cells, validated in primate recordings.

The paper introduces an accelerated version of spike-triggered non-negative matrix factorization to identify coordinated subunit mosaics in primate ganglion cells.

This study compares predictive models for retinal ganglion cell responses, finding that CNNs outperform LN models, while LN models show better cross-stimulus generalization.

The paper investigates nonlinear signal processing in retinal bipolar cells using salamander retinas and finds nonlinear contrast and spatial integration properties in a fraction of cells.