Fast read-out of object identity from macaque inferior temporal cortex
Supplementary Web Material
Chou P. Hung*, Gabriel Kreiman*, Tomaso Poggio, James J. DiCarlo
Center for Biological and Computational Learning and Computation and Systems Biology Initiative
McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology
*These two authors contributed equally to this work
1	Sample of spiking activity and local field potentials in macaque inferior temporal cortex
	Sample of spiking responses
	Sampel of LFP responses
2	Unsupervised clustering of neuronal responses yields similar categories
	Correlation coefficient matrix for population responses to two pictures
	Clustering of correlation coefficient matrix
	Clustering of correlation coefficient matrix with different numbers of clusters (n=2, 3, …, 10)
3	What is a category?
	Random group assignment
	Classifier performance versus similarity
	Confusion matrix
	Extrapolation to other pictures within a category
4	Read out of scale and position
	Classifier performance for reading out scale and position
	Latency of scale and position read-out
	Overlap in high SNR sites for group read-out, scale read-out and location read-out
5	Categorization and identification
	Read out performance for categorization and identification
	Latency of categorization and identification
	Latency of categorization and identification, invariance
	There was a strong overlap between the best neurons for the categorization and identification tasks
	There was a strong correlation in the categorization SNR and the identification SNR
6	Spike sorting
	Spike sorting and classifier performance
	Separation of single units and multi-units
7	Codes
	A family of codes parametrized by the spike count window
	Single bins
	Bursts of spikes
8	Robustness
	Robustness to neuronal drop-out during testing
	Robustness to failures in information transmission
9	Wiring specificity
	Wiring specificity
11	Neuronally plausible decoding
	Neuronally plausible statistical classifier and comparison of different statistical classifiers
12	Invariance to scale and location
	Robustness of the classifier to scale and location changes
	Invariance for novel images
	Novel objects images
	Invariance performance as a function of the number of sites
	Invariance for C2 units of the standard model
13	Detection of stimulus onset
	Detection of stimulus onset
14	Classification from simple image properties
	Stimulus categorization from simple image properties
15	Dependence on the number of sites
	Classifier performance versus number of sites with fits
	Number of neurons required to discriminate a number of objects at a fixed performance
16	Active versus passive viewing
	Discriminability for active versus passive viewing
gk, last updated
11/9/05
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