Walter J. Scheirer

This paper applies Extreme Value Theory to model decision boundaries in visual recognition, showing that Weibull fits better predict human category shifts in both 1D and 2D stimuli compared to central tendency models.

The paper uses machine learning and optogenetics to generate perceptual images from IT cortex stimulation, revealing subjective visual hallucinations.

The paper proposes a computational model using extreme value theory to explain how olfactory input modulates visual system function in zebrafish by integrating cross-modal sensory information.

This paper introduces a fast, learning-free method for sparse segmentation and reconstruction of neural volumes, demonstrating high-quality results across multiple imaging modalities.