{\rtf1\ansi\deff0\deftab360 {\fonttbl {\f0\fswiss\fcharset0 Arial} {\f1\froman\fcharset0 Times New Roman} {\f2\fswiss\fcharset0 Verdana} {\f3\froman\fcharset2 Symbol} } {\colortbl; \red0\green0\blue0; } {\info {\author Biblio 7.x}{\operator }{\title Biblio RTF Export}} \f1\fs24 \paperw11907\paperh16839 \pgncont\pgndec\pgnstarts1\pgnrestart Jia X, Hong H, DiCarlo JJ. Unsupervised changes in core object recognition behavior are predicted by neural plasticity in inferior temporal cortex. eLife. 2021;10. doi:10.7554/eLife.60830.\par \par Schrimpf M, Kubilius J, Hong H, et al. Using Brain-Score to Evaluate and Build Neural Networks for Brain-Like Object Recognition. In: Computational and Systems Neuroscience (COSYNE). Computational and Systems Neuroscience (COSYNE). Denver, CO; 2019.\par \par Hong H, Yamins DLK, Majaj NJ, DiCarlo JJ. Explicit information for category-orthogonal object properties increases along the ventral stream. Nature Neuroscience. 2016;19(4):613 - 622. doi:10.1038/nn.4247.\par \par Cadieu CF, Hong H, Yamins DLK, et al. Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition. Bethge M. PLoS Computational Biology. 2014;10(12):e1003963. doi:10.1371/journal.pcbi.1003963.\par \par Majaj NJ, Hong H, Solomon EA, DiCarlo JJ. A unified neuronal population code fully explains human object recognition. In: Computation and Systems Neuroscience (COSYNE). Computation and Systems Neuroscience (COSYNE). Salt Lake City, Utah, USA; 2012. doi:http://www.cosyne.org/c/index.php?title=Cosyne_12.\par \par }