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Area of Science:

  • Computational neuroscience
  • Artificial neural networks
  • Deep learning

Background:

  • Neural variability impacts information encoding in computational neuroscience.
  • Deep neural networks, particularly with Monte Carlo dropout, show variable responses to fixed inputs.
  • The structure and role of trial-by-trial neural covariance in dropout networks remain unstudied.

Purpose of the Study:

  • Investigate the structure of trial-by-trial neural covariance in convolutional neural networks with dropout.
  • Determine the role of this noise covariance in decoding accuracy.
  • Explore the relationship between noise and signal covariance in these networks.

Main Methods:

  • Utilized a convolutional neural network model incorporating dropout during training and testing.
  • Analyzed trial-by-trial correlations (noise correlation) between neurons.
  • Examined the alignment of covariance matrix axes and employed a trial-shuffling procedure.

Main Results:

  • Identified positive and low-dimensional trial-by-trial noise correlation in dropout networks.
  • Observed higher noise correlation between nearby neurons in feature maps, mirroring visual cortex findings.
  • Found that noise covariance subspace is shared across different images and aligned with global signal covariance.

Conclusions:

  • Dropout layers in neural networks introduce structured noise correlations with properties similar to biological neural systems.
  • The alignment of noise and signal covariance suggests a potential mechanism for reduced network accuracy.
  • Dropout networks may serve as computational models for neural variability and offer insights into information processing in the brain.