RT Journal Article SR Electronic T1 A Balanced Comparison of Object Invariances in Monkey IT Neurons JF eneuro JO eNeuro FD Society for Neuroscience SP ENEURO.0333-16.2017 DO 10.1523/ENEURO.0333-16.2017 VO 4 IS 2 A1 N. Apurva Ratan Murty A1 Sripati P. Arun YR 2017 UL http://www.eneuro.org/content/4/2/ENEURO.0333-16.2017.abstract AB Our ability to recognize objects across variations in size, position, or rotation is based on invariant object representations in higher visual cortex. However, we know little about how these invariances are related. Are some invariances harder than others? Do some invariances arise faster than others? These comparisons can be made only upon equating image changes across transformations. Here, we targeted invariant neural representations in the monkey inferotemporal (IT) cortex using object images with balanced changes in size, position, and rotation. Across the recorded population, IT neurons generalized across size and position both stronger and faster than to rotations in the image plane as well as in depth. We obtained a similar ordering of invariances in deep neural networks but not in low-level visual representations. Thus, invariant neural representations dynamically evolve in a temporal order reflective of their underlying computational complexity.