7 citations · 11 across the 5 of their papers we have counts for
5 papers
Efficient coding along the visual hierarchy
Ananya Passi, Brian S. Robinson, Michael F. Bonner
Biological visual systems learn from limited experience, unlike deep learning models that rely on millions of training images. What learning principles make this possible? We teste…
Characterizing Universal Object Representations Across Vision Models
Florian P. Mahner, Johannes Roth, Ka Chun Lam +3
Deep neural networks trained with different architectures, objectives, and datasets have been reported to converge on similar visual representations. However, what remains unknown…
Extremely coarse learning objectives induce human-aligned representations in AI vision models
Yash Mehta, Michael F. Bonner
Artificial neural networks trained on visual tasks develop internal representations resembling those of the primate visual system, a discovery that has guided a decade of computati…
High-dimensional structure underlying individual differences in naturalistic visual experience
Chihye Han, Michael F. Bonner
How do different brains create unique visual experiences from identical sensory input? While neural representations vary across individuals, the fundamental architecture underlying…
Universal scale-free representations in human visual cortex
Raj Magesh Gauthaman, Brice Ménard, Michael F. Bonner
How does the human brain encode complex visual information? While previous research has characterized individual dimensions of visual representation in cortex, we still lack a comp…