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Neural networks and perceptual learning

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.

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Figure 1: Example of perceptual learning involving a three-line bisection task.
Figure 2: Three-layer feedforward network model of a vernier hyperacuity task.
Figure 3: A schematic representation of a cortical column, consisting of two interconnected excitatory (E) and inhibitory (I) subpopulations, with modifiable intrinsic connections — used here to model contrast discrimination.
Figure 4: Learning on a texture discrimination task.

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Acknowledgements

We thank W. Li, V. Piech, D. Sagi and K. Pawelzik for their suggestions on the manuscript. M.T. is supported by Israeli Science Foundation and Irving B. Harris foundation. C.G. is supported by NIH.

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Tsodyks, M., Gilbert, C. Neural networks and perceptual learning. Nature 431, 775–781 (2004). https://doi.org/10.1038/nature03013

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