Max F. Burg

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.

This study uses convolutional neural networks to investigate wide-field inhibition in the marmoset retina, revealing hidden computational aspects of retinal processing through interpretable models.

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 uses digital twins to characterize the functional organization of mouse V1, revealing a combinatorial code of nonlinear response properties across 30 modes.

The paper develops a divisive normalization model informed by V1 neuron responses to natural images, showing orientation-specific normalization within receptive fields.