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Visual experience shapes neural representations. Networks trained on specific orientations develop biases, showing better discrimination for common visual features, supporting efficient coding principles.

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Area of Science:

  • Computational Neuroscience
  • Computer Vision
  • Visual Perception

Background:

  • Human and animal visual systems exhibit superior acuity for cardinal (horizontal/vertical) orientations.
  • This phenomenon is often attributed to efficient coding, where neural representations are optimized for prevalent orientations in natural scenes.
  • The role of experience alone in shaping these orientation biases remains an open question.

Purpose of the Study:

  • To investigate whether experience with a non-uniform distribution of orientations can induce biased neural representations.
  • To test the efficient coding hypothesis in a controlled artificial neural network model.
  • To examine the emergence and progression of orientation biases in deep neural networks.

Main Methods:

  • Utilized a convolutional neural network (VGG-16) trained on modified ImageNet datasets with specific orientation biases (0°, 22.5°, 45° rotations).
  • Measured orientation discriminability and neural unit tuning properties within different layers of the trained networks.
  • Analyzed the relationship between training data orientation distribution and network representations.

Main Results:

  • Network models showed highest orientation discriminability for orientations overrepresented in their training data.
  • An overrepresentation of narrowly tuned units selective for the most common training orientations was observed.
  • These orientation biases emerged in middle network layers, increased with depth, and appeared early in training.

Conclusions:

  • Biased orientation representations can arise solely from experience with non-uniform visual input.
  • These findings support the efficient coding hypothesis by demonstrating that artificial neural networks develop specialized representations based on statistical regularities in their training data.
  • Non-uniform representations may confer functional advantages for task performance in artificial visual systems.