Artificial Intelligence (AI) systems based solely on neural networks or symbolic computation present a representational complexity challenge. While minimal representations can produce behavioral outputs like locomotion or simple decision-making, more elaborate internal representations might offer a richer variety of behaviors. We propose that these issues can be addressed with a computational approach we call meta-brain models. Meta-brain models are embodied hybrid models that include layered components featuring varying degrees of representational complexity. We will propose combinations of layers composed using specialized types of models. Rather than using a generic black box approach to unify each component, this relationship mimics systems like the neocortical-thalamic system relationship of the mammalian brain, which utilizes both feedforward and feedback connectivity to facilitate functional communication. Importantly, the relationship between layers can be made anatomically explicit. This allows for structural specificity that can be incorporated into the model's function in interesting ways. We will propose several types of layers that might be functionally integrated into agents that perform unique types of tasks, from agents that simultaneously perform morphogenesis and perception, to agents that undergo morphogenesis and the acquisition of conceptual representations simultaneously. Our approach to meta-brain models involves creating models with different degrees of representational complexity, creating a layered meta-architecture that mimics the structural and functional heterogeneity of biological brains, and an input/output methodology flexible enough to accommodate cognitive functions, social interactions, and adaptive behaviors more generally. We will conclude by proposing next steps in the development of this flexible and open-source approach.
翻译：仅以神经网络或象征性计算为基础的人工智能(AI)系统是一个代表性复杂挑战。虽然最起码的表达方式可以产生运动或简单的决策等行为产出,但更复杂的内部陈述方式可以提供更丰富的行为形式。我们建议,这些问题可以通过一种我们称之为元脑模型的计算方法加以解决。元脑模型包含混合模型,其中包括具有不同程度代表性复杂性的分层组成部分。我们将提出使用特殊类型模型组成的多层组合。而不是使用通用黑盒方法来统一每个组成部分,这种关系模拟系统,例如哺乳动物大脑的神经皮层-细胞系统关系,它利用进料和反馈连接来便利功能性沟通。重要的是,层之间的关系可以从解剖上明确。这可以使结构特性具有可融入模型功能功能性,以有趣的方式纳入模型的功能性功能性功能性功能性。我们将提出若干类型的层次,可以执行独特的任务类型,从同时进行感应和感知的媒介,到下一个分子的形态-感官-感官系统关系,利用进料性和反馈的连接性连接性连接性连接性连接性交流。重要的是,两层之间的关系可以以解剖析为一种不同的结构结构结构结构,从而得出一个结构结构结构结构结构结构性分析方法。我们通过建立一个结构-结构结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-方法的形成-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构-结构
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