Dynamic Population Coding of Category Information in ITC and PFC

Ethan M. Meyers1,2, David J. Freedman3,4, Gabriel Kreiman2,5, Earl K. Miller1,3, Tomaso Poggio1,2

1. Department of Brain and Cognitive Sciences, MIT;   2. The McGovern Institute for Brain Research, MIT;   3.  The Picower Institute for Learning and Memory, RIKEN-MIT Neuroscience Research Center;  4.  Department of Neurobiology, The University of Chicago;  5.  Ophthalmology and Program in Neuroscience, Children's Hospital Boston, Harvard Medical School

Supplementary Web Material

Methods

Comparison of different classifiers on decoding 'abstract' category information

Comparison of different data normalization methods on decoding performance

Decoding including and excluding neurons that have temporal trends over trials

'Abstract' Categorization

Within-category decoding compared to mixed-category decoding

Within-category identity decoding compared to mixed-category identity decoding

Model units analyses

Decoding the outputs of computation model units for Serre et al. (2007)

Category Selective Index analyses

Comparison of decoding results and the Category Selective Index results reported in Freedman et al. (2003)

Movies

Movie of the correlation of the neural population to different images over time

Movie showing the dynamic coding of category information

Links

Link to the Freedman et al. 2003 J. Neuroscience paper

Link to the Population Decoding Toolbox