Results of unsupervised clustering of the neuronal similarity matrix. We defined the neuronal similarity between two pictures i and j based on the spiking population activity as the Pearson correlation coefficient between the vectors containing responses of all neurons (n=367) to picture i and picture j (see Figure with neuronal similarities). The responses consisted of the spike count in the [100;300] ms interval after stimulus onset. The dimensions of the resulting similarity matrix were 77 x 77 (symmetric matrix). We performed a k-means clustering algorithm (with 10 iterations and random initial conditions) on the neuronal similarity matrix. The rectangles delimit the pictures belonging to the same cluster.
Results with 2 clusters
Results with 3 clusters
Results with 4 clusters
Results with 5 clusters
Results with 6 clusters
Results with 7 clusters
Results with 8 clusters
Results with 9 clusters
Results with 10 clusters