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Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
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Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
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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.

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|November 14, 2023
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Summary
This summary is machine-generated.

We introduce Feedback-Feedforward Alignment (FFA), a novel learning algorithm that enables visual inference through pathway alignment. FFA demonstrates emergent capabilities like denoising and imagination, offering a bio-plausible alternative to traditional methods.

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Area of Science:

  • Neuroscience
  • Computer Science
  • Artificial Intelligence

Background:

  • Natural vision utilizes feedback connections for flexible inference, handling noisy or occluded data and enabling top-down processes like imagination.
  • The precise learning mechanisms enabling these feedback-driven capabilities remain unclear.
  • Understanding these mechanisms is crucial for advancing artificial visual systems.

Approach:

  • We propose Feedback-Feedforward Alignment (FFA), a learning algorithm that aligns feedforward and feedback pathways.
  • FFA uses mutual credit assignment computational graphs for co-optimization of distinct pathway objectives.
  • This approach was tested on MNIST and CIFAR10 datasets for classification and reconstruction tasks.

Key Points:

  • FFA successfully co-optimizes classification and reconstruction, demonstrating effective pathway alignment.
  • The alignment mechanism endows feedback connections with emergent visual inference functions: denoising, occlusion resolution, hallucination, and imagination.
  • FFA presents a bio-plausible alternative to back-propagation (BP), mitigating weight transport issues.

Conclusions:

  • FFA serves as a proof-of-concept for how feedback connections in the visual cortex achieve flexible visual functions.
  • This work advances the understanding of visual inference and perceptual phenomena.
  • FFA has implications for developing more biologically inspired and efficient learning algorithms.