A computational perspective on autism

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.

Keywords: Bayesian inference; E/I imbalance; autism; divisive normalization; neural computation.

Fig. 1.

Increasing prevalence of autism and research on the disorder. The incidence of autism (black curve) is compiled from studies by Wing and Gould (1), Newschaffer et al. (2), and the Centers for Disease Control and Prevention (3, 4). Paralleling this rapid rise in prevalence is increased research on the disorder. The number of publications in which “autism” appears in any PubMed field (blue curve) is shown for every year from 1946 to 2014.

Fig. 2.

The effect of divisive normalization parameters ν and c on simulated neural responses. Neural responses (R) are plotted as a function of the excitatory input (D). The equation describing the response functions is shown in A. For simplification, the suppressive field is set equal to D. (A) Changing the semisaturation constant (ν). Decreasing ν (with constant c) increases the E/I ratio, resulting in responses that saturate at lower values of D. (B) Changing the suppressive field gain term (c). Decreasing c (with constant ν) increases the E/I ratio, resulting in an overall increase in response amplitude.

Fig. 3.

Effects of divisive normalization on a model of primary visual cortex. (A) Receptive fields of five neurons with different retinotopic positions and orientations. (B) Contrast response function without (– D.N.) and with (+ D.N.) divisive normalization for a model neuron. The stimuli were gratings of the optimal position, orientation, and size for that neuron. Divisive normalization causes the response to saturate with increasing contrast. (C) Cross-orientation suppression in the same neuron. The plot shows responses to stimuli constructed by summing the preferred grating at 50% contrast and an orthogonal grating (“mask”) of different contrasts. Without divisive normalization, the response is unaffected by the mask. With divisive normalization, the mask has a suppressive effect that increases with mask contrast. (D) Size tuning for the same neuron. Without divisive normalization, the response increases monotonically with stimulus size and saturates. The saturation reflects that the neuron is activated equally well by any stimulus larger than its receptive field. With divisive normalization, the response first increases with stimulus size but then decreases, resulting in a preferred size. The decrease in activity reflects that larger stimuli activate more neurons, thereby increasing the suppressive effect of divisive normalization. (B–D) Response amplitudes are inherently smaller with than without divisive normalization. To highlight differences in the shapes of the response functions, each curve is plotted as a percentage of its maximum value.

Fig. 4.

Simulation 1: visual spatial suppression. (A) Psychophysical data showing that the ability to judge direction of motion decreases as stimulus size increases for high contrast stimuli (40). This is true for both typically developing controls (TD; red) and subjects with autism (ASD; blue), but ASD subjects consistently outperform TD subjects. Larger inverse thresholds indicate better performance. (B) Psychophysical data showing that for a small stimulus, ASD and TD subjects perform equivalently in judging direction of motion for a low-contrast stimulus, but ASD subjects perform better when the stimulus has a high contrast. (C) Simulation results showing population gains for the control (red) and autism (blue) models as a function of stimulus size for high contrast stimuli. The models’ responses follow the same pattern as the psychophysical data in A. (D) Simulation results for the control and autism models as a function of stimulus contrast for small stimuli. The models’ responses follow the same pattern as the psychophysical data in B.

Fig. 5.

Simulation 2: tunnel vision. (A) Psychophysical data showing that performance worsens as the target distance from the cue increases for both typically developing controls (TD; red) and subjects with autism (ASD; blue). Larger relative performance scores indicate faster, more accurate detection. Note that the rate at which performance decays is greater for ASD than TD subjects (there is greater overall change). (B) Psychophysical data showing that the performance gradient increases with the degree of autism symptomatology assessed using the autism spectrum quotient (AQ). ASD subjects (blue points) were identified based on Autism Diagnostic Observation Schedule scores. (C) Simulation results showing population gains for the control (red) and autism (blue) models as a function of target distance from the cue. The models’ responses follow the same pattern as the psychophysical data in A and further reproduce the nonmonotonic shape of the attentional field (44). To highlight the gradient difference between the control and autism models, the y axes are shifted to align the troughs of the curves. (D) Simulation results showing that, as the suppressive field gain term decreases (simulating an increasing degree of autism symptomatology), the gradient of the population gain increases, consistent with the psychophysical data in B. The colored dots correspond to the control and autism models in C.