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Neural knowledge assembly in humans and neural networks.

Stephanie Nelli1, Lukas Braun2, Tsvetomira Dumbalska2

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New information can rapidly reshape human understanding by reorganizing neural codes for object relations. This study demonstrates rapid neural manifold rearrangement in the brain and a computational model, enabling flexible knowledge assembly.

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Machine Learning

Background:

  • Human understanding adapts flexibly to new information, a process termed knowledge assembly.
  • Existing computational theories lack mechanisms for rapid reorganization of neural codes underlying this flexibility.

Purpose of the Study:

  • To investigate the neural mechanisms of rapid knowledge assembly.
  • To develop a computational model capable of similar knowledge reorganization.

Main Methods:

  • Participants learned object orderings in distinct contexts, followed by new linking information.
  • Blood-oxygen-level-dependent (BOLD) signals were measured during new information exposure.
  • Online stochastic gradient descent was adapted for a neural network model.

Main Results:

  • BOLD signals showed rapid and dramatic rearrangement of object representations on the neural manifold.
  • The adapted neural network model demonstrated rapid knowledge assembly capabilities.

Conclusions:

  • The dorsal frontoparietal cortex plays a key role in rapid knowledge assembly.
  • Computational models can replicate the brain's ability to quickly reorganize knowledge representations.