TY - JOUR TI - 'Artiphysiology' reveals V4-like shape tuning in a deep network trained for image classification AU - Pospisil, Dean A AU - Pasupathy, Anitha AU - Bair, Wyeth A2 - Vaadia, Eilon A2 - Gold, Joshua I VL - 7 PY - 2018 DA - 2018/12/20 SP - e38242 C1 - eLife 2018;7:e38242 DO - 10.7554/eLife.38242 UR - https://doi.org/10.7554/eLife.38242 AB - Deep networks provide a potentially rich interconnection between neuroscientific and artificial approaches to understanding visual intelligence, but the relationship between artificial and neural representations of complex visual form has not been elucidated at the level of single-unit selectivity. Taking the approach of an electrophysiologist to characterizing single CNN units, we found many units exhibit translation-invariant boundary curvature selectivity approaching that of exemplar neurons in the primate mid-level visual area V4. For some V4-like units, particularly in middle layers, the natural images that drove them best were qualitatively consistent with selectivity for object boundaries. Our results identify a novel image-computable model for V4 boundary curvature selectivity and suggest that such a representation may begin to emerge within an artificial network trained for image categorization, even though boundary information was not provided during training. This raises the possibility that single-unit selectivity in CNNs will become a guide for understanding sensory cortex. KW - neural network KW - V4 KW - shape perception KW - ventral stream JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -