  Connecting brain and behavior is a longstanding issue in the areas of
behavioral science, artificial intelligence, and neurobiology. As is standard
among models of artificial and biological neural networks, an analogue of the
fully mature brain is presented as a blank slate. However, this does not
consider the realities of biological development and developmental learning.
Our purpose is to model the development of an artificial organism that exhibits
complex behaviors. We introduce three alternate approaches to demonstrate how
developmental embodied agents can be implemented. The resulting developmental
BVs (dBVs) will generate behaviors ranging from stimulus responses to group
behavior that resembles collective motion. We will situate this work in the
domain of artificial brain networks along with broader themes such as embodied
cognition, feedback, and emergence. Our perspective is exemplified by three
software instantiations that demonstrate how a BV-genetic algorithm hybrid
model, multisensory Hebbian learning model, and multi-agent approaches can be
used to approach BV development. We introduce use cases such as optimized
spatial cognition (vehicle-genetic algorithm hybrid model), hinges connecting
behavioral and neural models (multisensory Hebbian learning model), and
cumulative classification (multi-agent approaches). In conclusion, we consider
future applications of the developmental neurosimulation approach.
