  There is much to learn through synthesis of Developmental Biology, Cognitive
Science and Computational Modeling. One lesson we can learn from this
perspective is that the initialization of intelligent programs cannot solely
rely on manipulation of numerous parameters. Our path forward is to present a
design for developmentally-inspired learning agents based on the Braitenberg
Vehicle. Using these agents to exemplify artificial embodied intelligence, we
move closer to modeling embodied experience and morphogenetic growth as
components of cognitive developmental capacity. We consider various factors
regarding biological and cognitive development which influence the generation
of adult phenotypes and the contingency of available developmental pathways.
These mechanisms produce emergent connectivity with shifting weights and
adaptive network topography, thus illustrating the importance of developmental
processes in training neural networks. This approach provides a blueprint for
adaptive agent behavior that might result from a developmental approach: namely
by exploiting critical periods or growth and acquisition, an explicitly
embodied network architecture, and a distinction between the assembly of neural
networks and active learning on these networks.
