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Neurally plausible mechanisms for learning selective and invariant representations.

Fabio Anselmi1,2,3, Ankit Patel4,5, Lorenzo Rosasco6

  • 1Center for Neuroscience and Artificial Intelligence Department of Neuroscience, Baylor College of Medicine, Baylor Plaza, 77030, Houston, USA. Fabio.Anselmi@bcm.edu.

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This study shows unsupervised learning, like Hebbian learning, can create object recognition invariant to transformations. This bridges the gap between artificial neural networks and brain-inspired visual processing models.

Keywords:
Group theoryHebbian learningInvariance

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Computer Vision

Background:

  • The ventral stream's neural coding achieves invariance to transformations that preserve object identity.
  • Artificial convolutional neural networks (CNNs) excel at learning invariant representations and predicting brain activity.
  • Neurally implausible mechanisms like backpropagation limit CNNs' relevance to biological vision.

Purpose of the Study:

  • To investigate neurally plausible unsupervised learning for invariant object representations.
  • To bridge the gap between CNNs and brain-based theories of visual invariance.
  • To explore unsupervised learning rules in simple-complex cell models.

Main Methods:

  • Utilized a simple-complex cell model.
  • Applied a broad class of unsupervised learning rules, including Hebbian learning.
  • Analyzed invariance to transformations within a finite orthogonal group.

Main Results:

  • Demonstrated that unsupervised learning rules can generate object representations invariant to specific nuisance transformations.
  • Showed Hebbian learning can contribute to building invariant visual features.
  • Validated the potential of unsupervised learning for achieving neural invariance.

Conclusions:

  • Unsupervised learning, particularly Hebbian learning, offers a neurally plausible pathway to invariant object representations.
  • Findings support the development of biologically constrained models for visual cortex and artificial intelligence.
  • This work advances understanding of how the brain and AI achieve object discrimination and transformation invariance.