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Engineering neural systems for high-level problem solving.

Jared Sylvester1, James Reggia1

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Summary
This summary is machine-generated.

We developed galis, a neural framework integrating top-down control with bottom-up neural computation. This approach successfully solves complex problems, matching human and symbolic AI performance in card matching tasks.

Keywords:
Attractor neural networksGated neural networksNeural network problem solvingNeuroengineeringTop-down vs. bottom-up AI

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Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Neuroscience

Background:

  • The debate between symbolic (top-down) and neural (bottom-up) AI approaches persists.
  • Neural networks excel at low-level tasks but struggle with high-level cognition (planning, reasoning).
  • Symbolic AI is effective for high-level tasks but lacks the adaptability of neural networks.

Purpose of the Study:

  • To develop a purely neural framework, galis, integrating top-down control with bottom-up neural computation.
  • To address the limitations of purely neural networks in high-level cognitive tasks.
  • To demonstrate a novel approach for engineering intelligent machine behavior.

Main Methods:

  • Developed galis, a framework based on attractor networks.
  • Programmed galis with temporal sequences of instructions to control neural network activity, communication, and learning.
  • Tested galis on sequential card matching problems, comparing performance against human subjects and a symbolic AI algorithm.

Main Results:

  • Galis successfully solved sequential card matching problems, demonstrating effective top-down attention control and feature binding.
  • The model's performance qualitatively and quantitatively matched human subjects.
  • Galis's performance also aligned with a top-down symbolic algorithm control.

Conclusions:

  • The galis framework offers a promising approach for engineering purely neurocomputational systems.
  • This framework enables problem-solving in tasks requiring higher-level cognitive functions.
  • Integrating top-down control within neural networks bridges the gap between symbolic and connectionist AI.