The vastness of the design space that is created by the combination of numerous computational mechanisms, including machine learning, is an obstacle to creating artificial general intelligence (AGI). Brain-inspired AGI development; that is, the reduction of the design space to resemble a biological brain more closely, is a promising approach for solving this problem. However, it is difficult for an individual to design a software program that corresponds to the entire brain as the neuroscientific data that are required to understand the architecture of the brain are extensive and complicated. The whole-brain architecture approach divides the brain-inspired AGI development process into the task of designing the brain reference architecture (BRA), which provides the flow of information and a diagram of the corresponding components, and the task of developing each component using the BRA. This is known as BRA-driven development. Another difficulty lies in the extraction of the operating principles that are necessary for reproducing the cognitive–behavioral function of the brain from neuroscience data. Therefore, this study proposes structure-constrained interface decomposition (SCID), which is a hypothesis-building method for creating a hypothetical component diagram that is consistent with neuroscientific findings. The application of this approach has been initiated for constructing various regions of the brain. In the future, we will examine methods for evaluating the biological plausibility of brain-inspired software. This evaluation will also be used to prioritize different computational mechanisms, which should be integrated and associated with the same regions of the brain.

由包括机器学习在内的众多计算机制的组合所创造的广阔设计空间是创建通用人工智能 (AGI) 的障碍。受大脑启发的 AGI 开发；也就是说，减少设计空间以更接近生物大脑，是解决这个问题的一种很有前途的方法。然而，由于理解大脑结构所需的神经科学数据广泛而复杂，因此个人很难设计出与整个大脑相对应的软件程序。全脑架构方法将受脑启发的 AGI 开发过程划分为设计大脑参考架构 (BRA) 的任务，该架构提供信息流和相应组件的图表，以及使用 BRA 开发每个组件的任务。这被称为 BRA 驱动的开发。另一个困难在于从神经科学数据中提取再现大脑认知行为功能所必需的操作原理。因此，本研究提出了结构约束界面分解 (SCID)，这是一种建立假设的方法，用于创建与神经科学发现一致的假设组件图。这种方法的应用已经开始用于构建大脑的各个区域。将来，我们将研究评估类脑软件的生物学合理性的方法。该评估还将用于对不同的计算机制进行优先排序，