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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Deep neural networks capture texture sensitivity in V2.

Md Nasir Uddin Laskar1, Luis Gonzalo Sanchez Giraldo2, Odelia Schwartz1

  • 1Department of Computer Science, University of Miami, FL, USA.

Journal of Vision
|July 22, 2020
PubMed
Summary
This summary is machine-generated.

Deep convolutional neural networks (CNNs) show promise in modeling visual cortex. CNNs can capture texture sensitivity changes across early visual areas, aiding understanding of brain processing.

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Area of Science:

  • Computational neuroscience
  • Machine learning applied to neuroscience
  • Visual cortex processing

Background:

  • Deep convolutional neural networks (CNNs) trained on visual data exhibit an ability to predict visual cortical neuron responses.
  • Understanding the specific computational factors and hierarchical processing levels within CNNs that enable this predictive power remains a challenge.
  • Texture sensitivity in the secondary visual cortex (V2) provides a specific neurophysiological context for investigating these models.

Purpose of the Study:

  • To investigate the role of different computational layers and operations in CNNs for explaining texture sensitivity in the visual cortex.
  • To quantitatively assess the compatibility of CNN models with neural recordings from early visual areas, particularly V2.
  • To determine how factors like training, receptive field size, and specific computations (convolution, rectification, pooling, normalization) contribute to model-brain alignment.

Main Methods:

  • Utilized deep convolutional neural networks (CNNs) trained on visual objects.
  • Compared texture sensitivity of CNN layers with neurophysiological data from primary and secondary visual cortices (V1 and V2).
  • Developed a quantitative method to fit CNN model neurons to neural recordings and analyzed the impact of rectification, pooling, and normalization operations.

Main Results:

  • Initial CNN layers showed qualitative correspondence with early visual areas regarding texture sensitivity.
  • CNNs achieved compatibility with V2 data in the second layer after rectification, further improved by pooling, with minimal influence from normalization.
  • Higher CNN layers offered modest improvements, while random weights significantly reduced model-brain compatibility.

Conclusions:

  • CNNs are effective models for capturing hierarchical processing changes in early cortical areas.
  • This approach aids in identifying computations underlying hierarchical visual processing in the brain.
  • The study highlights the potential of CNNs to bridge computational modeling and neurophysiological data in understanding visual perception.