Top-down and bottom-up classification between areas in mouse cerebral cortex to connect machine learning modules on connectomes

Abstract

The Whole Brain Architecture (WBA) is considered to be a strong candidate for the computational cognitive architecture of an artificial general intelligence (AGI) computing platform which includes empirical neural circuit information of the entire brain. The WBA is constructed with the aim of developing a biologically plausible general-purpose artificial intelligence with can exert brain-like multiple cognitive functions and behaviors in a computational system. In this study, we created Whole Brain Connectomic Architecture (WBCA), which is based on the datasets of quantified experiment results in mouse brain provided by Allen Institute for Brain Science to construct a unified platform of WBA. Strengths and hierarchies of connections between brain areas were computed to the provided data and confirmed the consistency in well-studied connections with previous studies. We suggest that computational cognitive architecture defined by connectomic data can enhance the development of AGI algorithms.