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Connecting brain and behavior is a longstanding issue in the areas ofbehavioral science, artificial intelligence, and neurobiology. As is standardamong models of artificial and biological neural networks, an analogue of thefully mature brain is presented as a blank slate. However, this does notconsider the realities of biological development and developmental learning.Our purpose is to model the development of an artificial organism that exhibitscomplex behaviors. We introduce three alternate approaches to demonstrate howdevelopmental embodied agents can be implemented. The resulting developmentalBVs (dBVs) will generate behaviors ranging from stimulus responses to groupbehavior that resembles collective motion. We will situate this work in thedomain of artificial brain networks along with broader themes such as embodiedcognition, feedback, and emergence. Our perspective is exemplified by threesoftware instantiations that demonstrate how a BV-genetic algorithm hybridmodel, multisensory Hebbian learning model, and multi-agent approaches can beused to approach BV development. We introduce use cases such as optimizedspatial cognition (vehicle-genetic algorithm hybrid model), hinges connectingbehavioral and neural models (multisensory Hebbian learning model), andcumulative classification (multi-agent approaches). In conclusion, we considerfuture applications of the developmental neurosimulation approach.

Braitenberg Vehicles as Developmental Neurosimulation