There is much to learn through synthesis of Developmental Biology, Cognitive Science and Computational Modeling. Our path forward involves a design for developmentally-inspired learning agents based on Braitenberg Vehicles. 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. 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.

翻译：通过综合发育生物学、认知科学与计算建模，我们可以获得诸多启示。我们的研究路径涉及基于布雷滕贝格载具设计受发育启发的学习智能体。持续发育神经模拟使我们能够思考发育轨迹在连接神经系统形态发生、发育学习与可塑性等相关现象中的作用。由于与持续学习紧密相关，我们的方法深度整合了发育具身性，并可通过一类称为发育布雷滕贝格载具（dBVs）的智能体实现。dBVs的生命起始于一组未定义结构，这些结构将转化为包含身体、传感器、效应器与神经系统的智能体系统。该表型通过发育时序特征进行描述：包含明确的形态发生期、关键期与习得期（发育学习期）。我们进一步提出，网络形态发生可通过遗传算法实现，而发育学习可采用多种计算方法实现。该方法为适应性智能体行为提供了框架，这些行为可能源自发育路径：即通过利用关键期或生长与习得期、显式具身网络架构，以及神经元网络组装与在这些网络上的主动学习之间的区分。最后，我们将探讨不同时间尺度（从极短间隔（<100ms）到长期演化）下的智能体学习与发育。在具身智能体方法中实现发育、演化与学习的统一，是构建生物启发智能整体观的关键。