A computational perspective on autism

Ari Rosenberg, Jaclyn Sky Patterson, Dora Angelaki

Research output: Contribution to journalArticle

Abstract

Autism is a neurodevelopmental disorder that manifests as a heterogeneous set of social, cognitive, motor, and perceptual symptoms. This system-wide pervasiveness suggests that, rather than narrowly impacting individual systems such as affection or vision, autism may broadly alter neural computation. Here, we propose that alterations in nonlinear, canonical computations occurring throughout the brain may underlie the behavioral characteristics of autism. One such computation, called divisive normalization, balances a neuron's net excitation with inhibition reflecting the overall activity of the neuronal population. Through neural network simulations, we investigate how alterations in divisive normalization may give rise to autism symptomatology. Our findings show that a reduction in the amount of inhibition that occurs through divisive normalization can account for perceptual consequences of autism, consistent with the hypothesis of an increased ratio of neural excitation to inhibition (E/I) in the disorder. These results thus establish a bridge between an E/I imbalance and behavioral data on autism that is currently absent. Interestingly, our findings implicate the context-dependent, neuronal milieu as a key factor in autism symptomatology, with autism reflecting a less "social" neuronal population. Through a broader discussion of perceptual data, we further examine how altered divisive normalization may contribute to a wide array of the disorder's behavioral consequences. These analyses show how a computational framework can provide insights into the neural basis of autism and facilitate the generation of falsifiable hypotheses. A computational perspective on autism may help resolve debates within the field and aid in identifying physiological pathways to target in the treatment of the disorder.

Original languageEnglish (US)
Pages (from-to)9158-9165
Number of pages8
JournalProceedings of the National Academy of Sciences of the United States of America
Volume112
Issue number30
DOIs
StatePublished - Jul 28 2015

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Autistic Disorder
Population
Neurons
Inhibition (Psychology)
Brain

Keywords

  • Autism
  • Bayesian inference
  • Divisive normalization
  • E/I imbalance
  • Neural computation

ASJC Scopus subject areas

  • General

Cite this

A computational perspective on autism. / Rosenberg, Ari; Patterson, Jaclyn Sky; Angelaki, Dora.

In: Proceedings of the National Academy of Sciences of the United States of America, Vol. 112, No. 30, 28.07.2015, p. 9158-9165.

Research output: Contribution to journalArticle

Rosenberg, Ari ; Patterson, Jaclyn Sky ; Angelaki, Dora. / A computational perspective on autism. In: Proceedings of the National Academy of Sciences of the United States of America. 2015 ; Vol. 112, No. 30. pp. 9158-9165.
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