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Fig. 10.15. Prenatally established bias for learning faces. Plots (a) and b show the RFs for every third neuron from the FSA array, visualized as in Figure 10.12a. As the prenatally trained network learns from real images, the RFs morph smoothly into face prototypes, i.e. representations of average facial features and hair outlines (c). By postnatal iteration 30,000, nearly all neurons have learned facelike RFs, with very little effect from the background patterns or non-face objects (a). Postnatal learning is less uniform for the naive network, as can be seen in the RF snapshots in (d). In the end, many of the naive neurons do learn facelike RFs, but others become selective for general texture patterns, and some become selective for objects like the clock (b). Overall, the prenatally trained network is biased toward learning faces, while the initially uniform network more faithfully represents the environment. Thus, prenatal learning can allow the genome to guide development in a biologically relevant direction.