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Neural network representations become similar across tasks due to abstraction constraints. Their trajectories from raw data to high-level concepts predict representational similarity in visual systems.

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Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Computer vision

Background:

  • Artificial and biological neural systems exhibit similar input representations when optimized for comparable tasks.
  • In visual systems, task optimization, particularly for object recognition, may lead to representation similarities through abstraction development.

Approach:

  • A two-dimensional abstraction space was created to position neural representations based on their distance from pixel and class spaces.
  • The study analyzed the trajectories of representations in task-optimized visual neural networks within this abstraction space.

Key Points:

  • Representations in task-optimized visual neural networks initiate near the pixel space and progress towards higher abstractions like object categories.
  • Proximity within the defined abstraction space correlates with the similarity of neural representations across different visual systems.

Conclusions:

  • The observed gradual and similar changes in neural representations suggest that representational trajectory constraints drive similarity across diverse, task-optimized systems.
  • Understanding these abstraction hierarchies offers insights into the convergence of representational strategies in both artificial and biological visual systems.