The whole brain architecture approach: Accelerating the development of artificial general intelligence by referring to the brain

TL;DR

This paper proposes a brain-inspired development framework for artificial general intelligence, focusing on creating a brain reference architecture and a method to extract operating principles from neuroscience data.

Contribution

It introduces the Structure-constrained Interface Decomposition (SCID) method for hypothesizing brain component diagrams aligned with neuroscientific findings.

Findings

Application of SCID to various brain regions

Framework for evaluating biological plausibility of AI models

Prioritization of computational mechanisms based on neuroscience data

Abstract

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.