Whole Brain Connectomic Architecture (WBCA) is static and schematic WBA, which is a good candidate to build up artificial general intelligence (AGI), based on biological connectomes, or wiring diagrams of the brain. AGI development would become more efficient by constraining connections among machine learning modules with connectomic information rather than applying the architectures developers build by their own ways. We have released our ongoing prototype of the WBCA resulting from our analysis for the future implementation.
This repository has 4 directories, “Release”, “Codes”, “DataAnalysis” and “BlockDiagram”. “Release” directory includes the current version of WBCA (wbca_version.json), which is the main product described in BriCA Language,. “Codes” has all algorithms we developed for data analysis and representation. “DataAnalysis” consists of original raw data from Allen Institute for Brain Science, and analytical results processed by our algorithms. BlockDiagram has input and output files for mermaid.js (written by JavaScript) to illustrate a block diagram of the whole brain architecture.
The main product is neither executable nor functional as an artificial intelligence system. It is still under development, currently, no machine learning modules included with WBCA to exert cognitive functions. This initial version of the WBCA is possibly only applicable for implementing into a brain-like simulation.
・To be reviewed by professionals from the field of machine learning and neuroscience
・To broaden our views through public dialogue
・To deepen and spread our expertise
・To take the initiative to provide WBCA at the earliest possible time
・Python 2.7
haruom
skyair55
businy
Hiroto Tamura
rinkom
rondelion
hymkw
copied from Allen Brain Atlas website
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We have used the BriCA Language to mock up the WBCA.
The BriCA Language is a Domain Specific Language (DSL)/Architecture Description Language (ADL) for describing modules and message passing routes in computing architecture. More specifically, it describes modules, ports on the modules, and connections among the ports. The language enables users to record, exchange, and modify architecture design.
In this product, the module corresponds to the brain region annotated by Allen Institute for Brain Science. The port size is a representation of the diameter of neural fibers (axons), which is nearly equal to the neural connectivity strengths.
Allen Brain Atlas SDK (http://alleninstitute.github.io/AllenSDK/)
The Allen Institute provides a software development kit, the Allen Brain Atlas SDK, to collect data for neural connectivity analysis between brain regions. We have utilized the classes of Mouse Connectivity and Reference Space from Allen Brain Atlas SDK in order to create mouse brain connectome. Mouse Connectivity includes data for calculating connectivity strength, feedforward and feedback information flows, etc. Reference Space contains data to get a number of voxels (volume). Below is a data flow diagram for Mouse Brain Connectivity Atlas about the parameters we utilize to build WBCA.
