使用具身计算智能体的持续发育神经模拟
Continual Developmental Neurosimulation Using Embodied Computational Agents
AI总结：
该研究提出基于发育型布雷特纳吉车辆的持续发育神经模拟框架，结合遗传算法实现网络形态发生与多方法发育学习，为生物启发智能的整合研究提供新思路。
摘要：
通过整合发育生物学、认知科学与计算建模，我们有大量内容有待探索。我们的研究路径提出了一种基于Braitenberg Vehicles（布雷特纳吉车辆）的、受发育启发的学习智能体设计。持续发育神经模拟使我们能够探究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。该方法与持续学习密切相关，与发育性具身性深度整合，可通过一种名为发育型布雷特纳吉车辆（developmental Braitenberg Vehicles, dBVs）的智能体实现。\ndBVs最初以一组未定义结构的形式存在，随后会转变为包含身体、传感器、效应器与神经系统的基于智能体的系统。该表型通过发育时序定义特征：存在 distinct 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方法实现，而发育学习则可通过多种计算方法落地。\n该方法为自适应智能体行为提供了一个框架，这类行为可能源于发育路径：即通过利用关键期或生长与习得过程、明确的具身化网络架构，以及神经元网络组装与网络上主动学习之间的区分。最后，我们将在不同时间尺度下考察智能体学习与发育，从极短（<100ms）间隔到长期演化。具身智能体方法中的发育、演化与学习，是构建生物启发智能整合视角的关键。
英文摘要：
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.
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