  The vastness of the design space created by the combination of a large number
of computational mechanisms, including machine learning, is an obstacle to
creating an artificial general intelligence (AGI). Brain-inspired AGI
development, in other words, cutting down the design space to look more like a
biological brain, which is an existing model of a general intelligence, is a
promising plan for solving this problem. However, it is difficult for an
individual to design a software program that corresponds to the entire brain
because the neuroscientific data 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) -- the flow of information and the
diagram of corresponding components -- and the task of developing each
component using the BRA. This is called BRA-driven development. Another
difficulty lies in the extraction of the operating principles necessary for
reproducing the cognitive-behavioral function of the brain from neuroscience
data. Therefore, this study proposes the Structure-constrained Interface
Decomposition (SCID) method, which is a hypothesis-building method for creating
a hypothetical component diagram consistent with neuroscientific findings. The
application of this approach has begun for building various regions of the
brain. Moving forward, we will examine methods of evaluating the biological
plausibility of brain-inspired software. This evaluation will also be used to
prioritize different computational mechanisms, which should be merged,
associated with the same regions of the brain.
