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A computational perspective on autism
Contributed by Dora E. Angelaki, May 29, 2015 (sent for review February 25, 2015; reviewed by Odelia Schwartz and Reza Shadmehr)
This article has a Letter. Please see:
- A more precise look at context in autism - September 10, 2015
See related content:
- A synergistic approach to mental health research- Sep 10, 2015

Significance
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.
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.
Footnotes
- ↵1To whom correspondence may be addressed. Email: ari.rosenberg{at}wisc.edu or angelaki{at}bcm.edu.
This contribution is part of the special series of Inaugural Articles by members of the National Academy of Sciences elected in 2014.
Author contributions: A.R. and D.E.A. designed research; A.R. and J.S.P. performed research; A.R. and J.S.P. analyzed data; and A.R., J.S.P., and D.E.A. wrote the paper.
Reviewers: O.S., University of Miami; and R.S., Johns Hopkins University.
The authors declare no conflict of interest.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1510583112/-/DCSupplemental.
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- Evidence for an E/I Imbalance in Autism
- Connecting the E/I Balance to Neural Computation
- Connecting Divisive Normalization and Autism
- Simulation 1: Visual Spatial Suppression
- Simulation 2: Tunnel Vision
- Simulation 3: Neural Implementation of Bayesian Priors
- Local vs. Global Processing
- Simple vs. Complex Stimuli
- Multisensory Processing
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