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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Computer Vision

Background:

  • Face-selective neurons in the primate visual pathway are crucial for face detection.
  • A long-standing debate exists on whether this selectivity is innate or learned through visual experience.

Purpose of the Study:

  • To investigate if face selectivity can emerge in deep neural networks without any training.
  • To explore the potential for innate mechanisms driving visual selectivity in artificial neural networks.

Main Methods:

  • Utilized a hierarchical deep neural network model simulating the ventral visual stream.
  • Analyzed randomly initialized networks to observe the emergence of unit selectivity.
  • Assessed the functional capabilities of untrained networks in face detection tasks.

Main Results:

  • Face-selective units spontaneously emerged in randomly initialized networks.
  • These emergent units exhibited characteristics similar to those observed in primate brains.
  • The untrained network demonstrated face-detection capabilities due to innate selectivity.
  • Selectivity for non-face objects also arose innately in untrained networks.

Conclusions:

  • Random feedforward connections in untrained deep neural networks are sufficient for initializing primitive visual selectivity.
  • This suggests that innate mechanisms may play a significant role in the early stages of visual perception.
  • The findings challenge the necessity of extensive visual training for the emergence of basic visual feature selectivity.