Continual Developmental Neurosimulation Using Embodied Computational Agents

Abstract

There is much to learn through synthesis of Developmental Biology, Cognitive Science and Computational Modeling. Our path forward is to present a design for developmentally-inspired learning agents based on Braitenberg Vehicles. Using these agents to exemplify the embodied nature of computational autonomy, we move closer to modeling embodied experience and morphogenetic growth as components of cognitive developmental capacity. We consider biological and cognitive development which influence the generation of adult phenotypes and the contingency of available developmental pathways. Continual developmental neurosimulation allows us to consider the role of developmental trajectories in bridging the related phenomena of nervous system morphogenesis, developmental learning, and plasticity. Being closely tied to continual learning, our approach is tightly integrated with developmental embodiment, and can be implemented using a type of agent called developmental Braitenberg Vehicles (dBVs). dBVs begin their lives as a set of undefined structures that transform into agent-based systems including a body, sensors, effectors, and nervous system. This phenotype is characterized in terms of developmental timing: with distinct morphogenetic, critical, and acquisition (developmental learning) periods. These properties produce emergent connectivity with shifting weights and adaptive network topologies in the context of developmental processes and spatially-dependent embodiment in training a dBVs neuronal network. We further propose that network morphogenesis can be accomplished using a genetic algorithmic approach, while developmental learning can be implemented using a number of computational methodologies. This approach provides a framework 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 neuronal networks and active learning on these networks. In conclusion, we will consider agent learning and development at different timescales, from very short (<100ms) intervals to long-term evolution. The development, evolution, and learning in an embodied agent-based approach is key to an integrative view of biologically-inspired intelligence.