Justin L. Gardner

The paper identifies a common temporal motif in neural dynamics across the ventral visual stream and shows that current models fail to replicate these dynamics.

The paper models orientation selectivity in mouse superior colliculus to show it shares computational principles with primate visual cortex despite differences in expression.

The paper shows that mice can discriminate textures and that the secondary visual area (LM) encodes texture statistics more efficiently than the primary area (V1), linking stimulus complexity to neural representation and perception.

This paper investigates noise in line-scan MRI and shows that temporal fluctuations can mimic neural responses, highlighting limitations in detecting the DIANA response.

The paper investigates whether category-selective visual cortex represents objects or texture-like features, finding that models based on these regions fail to distinguish natural objects from scrambled textures.

This paper shows that making neural networks more robust to image perturbations aligns their features with macaque V1 neural responses and improves predictivity.

This paper develops a computational model of mouse visual cortex, finding that self-supervised contrastive learning produces better matches to mouse cortex than supervised methods, suggesting a resource-efficient, general-purpose visual system.

The paper investigates whether spatial and feature-based attention share computational mechanisms by analyzing perceptual improvements in estimation tasks.

This paper demonstrates that neural systems are inherently error-prone in information processing even under ideal conditions, revealing an innate precision limitation in neural information transformation.

The paper investigates how context influences probability estimation and identifies brain regions involved in this process.

The paper develops a quantitative framework to model how motion visibility parameters modulate human cortical responses across visual areas.