Article Information
History
- February 12, 2018.
Article Versions
- Version 1 (January 1, 2018 - 16:42).
- Version 2 (January 23, 2018 - 12:57).
- Version 3 (January 23, 2018 - 20:21).
- You are viewing Version 4, the most recent version of this article.
Copyright
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Author Information
- Rishi Rajalingham*,
- Elias B. Issa*,
- Pouya Bashivan,
- Kohitij Kar,
- Kailyn Schmidt and
- James J. DiCarlo
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
- Correspondence should be addressed to James J. DiCarlo, McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 77 Massachusetts Institute of Technology, 46-6161, Cambridge, MA 02139. E-mail: dicarlo{at}mit.edu
↵* R.R. and E.B.I. contributed equally to this work.
AUTHOR CONTRIBUTIONS: E.B.I., R.R., and J.J.D designed the experiments. E.B.I., K.S., R.R., and K.K. carried out the experiments. R.R., E.B.I., and P.B. performed the data analysis and modeling. R.R., E.B.I., and J.J.D. wrote the manuscript.
Posted February 12, 2018.
Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks
