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.A utism is a neurodevelopmental disorder that is dramatically increasing in prevalence (Fig. 1). Recent data place the number of children being diagnosed with autism in the United States at 1 in 68, more than doubling in the last decade (1-4). The disorder is highly pervasive, affecting individuals at cognitive, motor, and perceptual levels. It is furthermore a "spectrum disorder," with symptoms that manifest in varying degrees across individuals. This heterogeneity presents significant challenges to establishing a comprehensive characterization of the disorder.Research investigating the genetic and molecular basis of autism implicates over 100 genes (5), many of which are involved in synaptic development and function (6-8). As such, one prominent hypothesis is that autism arises from a neurophysiological excitation-to-inhibition (E/I) imbalance (9, 10). However, the connection between an E/I imbalance and the behavioral characteristics of the disorder remains unclear. Considering the pervasive nature of autism, and the covariance of loosely related symptoms (11-14), one possibility is that an E/I imbalance widely affects neural computation, in turn giving rise to the broad behavioral symptoms recognized as autism.Here,...

A more precise look at context in autism

“…In fact, increased sensory precision could arise from weaker lateral inhibition or gain control in the visual cortex, exactly as formulated in Rosenberg et al (1). Furthermore, even precise (or disinhibited) visual responses within a single level of the cortical hierarchy may still be down-weighted if the precision of predictions at a higher level (e.g., NMDAmediated synaptic gain) mandates this.…”

“…In a recent paper, Rosenberg et al (1) present a compelling explanation for the perceptual symptoms of autism in terms of a failure of divisive normalization. In divisive normalization the output of individual neurons is scaled (or divided) by the combined activity of the neural population in which they are embedded, and thus local visual context provides a means of gain control-a volume dial-for stimulus-evoked responses.…”

“…Furthermore, even precise (or disinhibited) visual responses within a single level of the cortical hierarchy may still be down-weighted if the precision of predictions at a higher level (e.g., NMDAmediated synaptic gain) mandates this. Top-down attenuation of sensory precision accounts for psychophysical and physiological aspects of attention (4), providing a hierarchically and biologically plausible account of the "attentional field" modeled in Rosenberg et al (1). Finally, beyond a single sensory event, adaptive perception and action rests on the accurate estimation of fluctuations in the precision of environmental contingencies (5).…”

Theory of cortical function

“…Consequently, it may be extremely useful to identify abnormal computations in individuals with particular psychiatric and neurodevelopmental disorders. For example, deficits in normalization (79), deficits in prediction (80), dysfunctional Bayesian inference (81), and uncontrolled neural response variability (82,83) have each been hypothesized to underlie autism.…”

Disentangling signal and noise in autism spectrum disorder

“…As part of the wider trend in computational psychiatry (Friston, Stephan, Montague, & Dolan, 2014;Montague, Dolan, Friston, & Dayan, 2012;, recent explanatory accounts of autism spectrum disorder (ASD) take inspiration from well-articulated information processing models of typical cognition (Hohwy, 2013;Lawson, Rees, & Friston, 2014;Pellicano & Burr, 2012;Qian & Lipkin, 2011;Quattrocki & Friston, 2014;Rosenberg, Patterson, & Angelaki, 2015;Sinha et al, 2014;Van de Cruys et al, 2014). Particularly influential in most of these new proposals for ASD is the predictive coding framework (Clark, 2013;Friston, 2010).…”