{\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 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 Kar K, Kubilius J, Schmidt K, Issa EB, DiCarlo JJ. Evidence that recurrent circuits are critical to the ventral stream's execution of core object recognition behavior. bioRxiv. 2018. doi:https://doi.org/10.1101/354753.\par \par Rajalingham R, Issa EB, Bashivan P, Kar K, Schmidt K, DiCarlo JJ. Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks. bioRxiv. 2018. doi:https://doi.org/10.1101/240614.\par \par Issa EB, Cadieu CF, DiCarlo JJ. Neural dynamics at successive stages of the ventral visual stream are consistent with hierarchical error signals. eLife. 2018;7. doi:10.7554/eLife.42870.\par \par }