The primacy model and the structure of olfactory space
Fig 4
Non-random low-dimensional structure in PN-KC connectivity that is conserved across animals.
A, B. Glomerulus-KC connectivity matrices from FlyEM and FAFB datasets. C, D: Glomerulus-glomerulus connectivity similarities (Pearson correlations of connectivities). E. Glomerulus-glomerulus connectivity similarities in two datasets against each other. The correlation in glomerulus-glomerulus connectivity similarities is r = 0.47 (p<0.01). F. Similarity between datasets disappears if one of the datasets is shuffled while preserving the connectivity matrix in- and out- degrees (right)[44]. We observed that the average correlations for bootstrapped connectivity data in which KCs were selected with repetitions is somewhat lower than for the intact data in panel E. G. Variance explained per dimension as a function of the PCA dimension (inset–total cumulative variance explained). PCA analysis shows that the first two linear dimensions are significantly different from random. H. Connectivity matrices in two datasets projected onto the first two dimensions. Points represent individual glomeruli. The same glomeruli in two datasets (different animals) are connected by black segments and reside near each other in the 2D embedding suggesting that the first two dimensions of the connectivity matrix are conserved across datasets. I, J. The same analysis using a non-linear low-dimensional embedding technique (Isomap) shows that the first two dimensions in the connectivity data are both different from random (I), explain more variance in the data than the linear algorithm (PCA), and are conserved across datasets (J). The number of nearest neighbors for the Isomap algorithm (inset) was chosen as described in Methods H in S1 Text.