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Never-Ending Learning for Explainable Brain Computing.

Hongzhi Kuai1,2, Jianhui Chen3,4, Xiaohui Tao5

  • 1Faculty of Engineering, Maebashi Institute of Technology, Gunma, 371-0816, Japan.

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Summary

This study introduces an explainable brain computing framework using a never-ending learning approach to unify cognitive neuroscience findings. It enhances understanding of human intelligence and behavior by integrating knowledge, information, and data for clearer brain activity interpretation.

Keywords:
evidence combination and fusion computingexplainable brain computingfunctional neuroimaginghigh‐order brain cognitionhuman‐in‐the‐loopnever‐ending learningthinking and reasoning

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Area of Science:

  • Cognitive Neuroscience
  • Artificial Intelligence
  • Neuroscience

Background:

  • Human intelligence and behavior exploration is complex due to fragmented findings in cognitive neuroscience.
  • A unified and transparent interpretation of diverse study results remains a significant challenge.

Purpose of the Study:

  • To propose an explainable brain computing framework for continuous cognitive neuroscience investigation.
  • To integrate evidence combination and fusion computing within a Knowledge-Information-Data (KID) architecture.
  • To enhance the understanding of brain activity patterns related to human intelligence.

Main Methods:

  • Employs a never-ending learning paradigm with a Knowledge-Information-Data (KID) architecture.
  • Utilizes joint knowledge-driven forward inference and data-driven reverse inference.
  • Incorporates internal evidence learning (multi-task neuroimaging) and external evidence learning (topic modeling of studies), with human-in-the-loop mechanisms.

Main Results:

  • Reveals intricate uncertainties in human reasoning brain localization through two case studies.
  • Demonstrates the framework's capability for continuous brain cognition investigation.
  • Highlights the potential of systematization to advance explainable brain computing.

Conclusions:

  • The proposed framework offers a path toward more systematic and explainable brain computing.
  • Advancements in systematization can lead to a finer-grained understanding of neural correlates of human intelligence.
  • The integration of multi-modal evidence and human interaction is crucial for robust cognitive modeling.