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Brain-like Flexible Visual Inference by Harnessing Feedback-Feedforward Alignment.

Tahereh Toosi1, Elias B Issa2

  • 1Center for Theoretical Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University New York, NY.

Advances in Neural Information Processing Systems
|November 18, 2024
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Summary
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We introduce Feedback-Feedforward Alignment (FFA), a novel learning algorithm that enables visual inference through pathway alignment. FFA demonstrates emergent functions like denoising and imagination, offering a bio-plausible alternative to traditional methods.

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Area of Science:

  • Computational neuroscience
  • Machine learning
  • Computer vision

Background:

  • Feedback connections in natural vision enable complex inference from noisy or occluded data.
  • The learning mechanisms for flexible top-down visual processing remain unclear.

Purpose of the Study:

  • To propose and validate a novel learning algorithm, Feedback-Feedforward Alignment (FFA), for emergent visual inference.
  • To investigate the co-optimization of feedforward and feedback pathways for enhanced visual capabilities.
  • To enhance the bio-plausibility of learning algorithms in artificial neural networks.

Main Methods:

  • Developed the Feedback-Feedforward Alignment (FFA) algorithm, utilizing mutual credit assignment computational graphs.
  • Co-optimized classification and reconstruction tasks on MNIST and CIFAR10 datasets.
  • Compared FFA's bio-plausibility against traditional back-propagation (BP) methods, addressing weight transport issues.

Main Results:

  • FFA demonstrated emergent visual inference functions, including denoising, occlusion resolution, hallucination, and imagination.
  • Successful co-optimization of tasks on MNIST and CIFAR10 datasets was achieved.
  • FFA showed improved bio-plausibility by repurposing credit assignment graphs and alleviating BP's weight transport problem.

Conclusions:

  • FFA provides a proof-of-concept for how feedback connections learn flexible visual functions.
  • The alignment mechanism in FFA enables emergent top-down processing capabilities.
  • FFA offers a biologically inspired alternative for developing advanced visual inference algorithms.