A computational perspective on autism

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  • Ari Rosenberg
    Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030
  • Jaclyn Sky Patterson
    Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030
  • Dora E. Angelaki
    Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030

Abstract

<jats:title>Significance</jats:title> <jats:p>Autism is a pervasive disorder that broadly impacts perceptual, cognitive, social, and motor functioning. Across individuals, the disorder manifests with a large degree of phenotypic diversity. Here, we propose that autism symptomatology reflects alterations in neural computation. Using neural network simulations, we show that a reduction in the amount of inhibition occurring through a computation called divisive normalization can account for perceptual consequences reported in autism, as well as proposed changes in the extent to which past experience influences the interpretation of current sensory information in individuals with the disorder. A computational perspective can help bridge our understandings of the genetic/molecular basis of autism and its behavioral characteristics, providing insights into the disorder and possible courses of treatment.</jats:p>

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