Figure - PMC

Skip to main content

An official website of the United States government

Here's how you know

Here's how you know

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

View full-text article in PMC

. 2014 Dec 18;10(12):e1003963. doi: 10.1371/journal.pcbi.1003963

Search in PMC
Search in PubMed
View in NLM Catalog
Add to search

© 2014 Cadieu et al

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

PMC Copyright notice

Testing set classification accuracy averaged over 10 randomly-sampled test sets is plotted and error bars indicate standard deviation over the 10 random samples. Chance performance is ∼14.3%. V4 and IT Cortex Multi-Unit Sample are the values measured directly from the neural samples. Following the analysis in Fig. 3A, the model representations have been modified such that they are both subsampled and have noise added that is matched to the observed IT multi-unit noise. We indicate this modification by the symbol. Both model and neural representations are subsampled to 80 multi-unit samples or 80 features. Mirroring the results using kernel analysis, the IT cortex multi-unit sample achieves high generalization accuracy and is only matched in performance by the Zeiler & Fergus 2013 representation.

Inline graphic — Testing set classification accuracy averaged over 10 randomly-sampled test sets is plotted and error bars indicate standard deviation over the 10 random samples. Chance performance is ∼14.3%. V4 and IT Cortex Multi-Unit Sample are the values measured directly from the neural samples. Following the analysis in Fig. 3A, the model representations have been modified such that they are both subsampled and have noise added that is matched to the observed IT multi-unit noise. We indicate this modification by the symbol. Both model and neural representations are subsampled to 80 multi-unit samples or 80 features. Mirroring the results using kernel analysis, the IT cortex multi-unit sample achieves high generalization accuracy and is only matched in performance by the Zeiler & Fergus 2013 representation.