Figure 7. Elastic net maps.

(A–D) Sample orientation maps trained on images of varying degrees of co-circularity (ε). ε = 0.0 corresponds to maps trained on highly co-circular inputs while ε = 1.0 signifies maps trained on inputs without co-circular correlations. (E–H) The associated P(β, α|r = 0) distributions for cortical points with small separation do not have reduced symmetry since pairs tend to be orientated similarly regardless of direction β. (I–L) The P(β, α|r = λ) distributions show forms of reduced symmetry. In the highly correlated training condition (I, ε = 0.0) the maps are strongly co-circular at τ = 0. As the training input correlations are reduced the strength of the co-circularity is reduced (J,K), but even with no correlation (L, ε = 1.0) the maps still exhibit reduced symmetry. However, with no input correlation, the maps show co-circularity with offset rather than co-circularity and the values for τ are randomly distributed.