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Toward a foundational brain model of intelligence.

Xiao-Jing Wang1

  • 1Center for Neural Science, New York University, 4 Washington Place, New York, NY 10003, USA.

Current Opinion in Neurobiology
|April 10, 2026
PubMed
Summary

Foundation models are being used in neuroscience to understand intelligence. This work proposes a computational platform for a biologically-based foundational brain model to advance artificial intelligence (AI).

Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence (AI)
  • NeuroAI

Background:

  • Foundation models, pre-trained on vast datasets, are increasingly applied in neuroscience.
  • Current AI systems struggle with complex cognitive abilities like reasoning and planning.
  • Mechanistic understanding of the brain remains a significant challenge.

Purpose of the Study:

  • To explore how foundation models can yield mechanistic understanding beyond mere predictions in neuroscience.
  • To propose a computational platform for a biologically-based foundational brain model of intelligence.
  • To advance artificial intelligence (AI) systems towards human-like general intelligence.

Main Methods:

  • Developing a computational platform integrating connectome and cell types.

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  • Modeling complex dynamics of recurrent neural circuits.
  • Training the model to perform multiple cognitive tasks to measure intelligence.

    • Main Results:

    • Outlines key requirements for a biologically-based foundational brain model.
    • Highlights the prefrontal cortex as central to cognitive capabilities.
    • Demonstrates potential through recent NeuroAI works.

    Conclusions:

    • Foundation models offer a path towards mechanistic understanding in neuroscience.
    • A biologically-based foundational brain model, centered on the prefrontal cortex, is proposed.
    • Novel algorithms derived from this approach could significantly advance general AI.