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Neural networks and perceptual learning

Author

Listed:
  • Misha Tsodyks

    (Weizmann Institute)

  • Charles Gilbert

    (The Rockefeller University)

Abstract

Sensory perception is a learned trait. The brain strategies we use to perceive the world are constantly modified by experience. With practice, we subconsciously become better at identifying familiar objects or distinguishing fine details in our environment. Current theoretical models simulate some properties of perceptual learning, but neglect the underlying cortical circuits. Future neural network models must incorporate the top-down alteration of cortical function by expectation or perceptual tasks. These newly found dynamic processes are challenging earlier views of static and feedforward processing of sensory information.

Suggested Citation

  • Misha Tsodyks & Charles Gilbert, 2004. "Neural networks and perceptual learning," Nature, Nature, vol. 431(7010), pages 775-781, October.
    • Handle: RePEc:nat:nature:v:431:y:2004:i:7010:d:10.1038_nature03013
    DOI: 10.1038/nature03013
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    Cited by:

    1. Daniel Bjasch & Christopher J Bockisch & Dominik Straumann & Alexander A Tarnutzer, 2012. "Differential Effects of Visual Feedback on Subjective Visual Vertical Accuracy and Precision," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-11, November.

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