Purpose: While a number of models examine the joint development of ocular dominance (OD) and orientation selective (OS) receptive fields (RF's) and maps in V1, few examine the development of other features in V1, and hardly any examine the development of feature maps and RF's in higher visual areas. In 1999 and 2000 (ARVO), we presented the first models of motion direction selective (DS) map and RF development in V1 and MT/V5, respectively, given moving bar stimuli. Here, we extended this model to examine the joint development of DS, OS, and OD maps and RF's in V1 and MT given noise and natural moving stimuli to simulate pre- and post-natal conditions. Methods: Our model involves 3 topographically connected layers of neurons corresponding to the LGN, V1, and MT. The LGN level is divided into Left-On, Left-Off, Right-On, and Right-Off channels, while V1 and MT are divided into separate lamina. To model DS, each spatial connection contains multiple time-delayed connections and our unique PCA-based, spatiotemporal learning rule. Functionally, our model is distinguished from other models in that it only uses local, biologically realistic mechanisms. Results: Given stationary stimuli, the model produces OS and OD maps and RF's with most of the major features observed in V1 (Blasdel, 1992). When given moving stimuli, the model produces DS RF's and maps with many of the features recently observed in MT (Diogo et al, 2003; Livingstone et al, 2001) and V1 (Emerson, 1997), which are analogous to those observed for OS (e.g. pinwheels, factures, linear zones). In addition, it produces a combined map of DS and OS cells, whose relationship provides testable predictions. Conclusion: These results demonstrate that computational principles employed for learning OS and OD (e.g. competitive Hebbian learning with faster-than-linear inhibition) can be generalized to learn more complex features in higher areas with only the addition of multiple layers and spatiotemporal learning.