  Theories for autism spectrum disorder (ASD) have been formulated at different
levels: ranging from physiological observations to perceptual and behavioral
descriptions. Understanding the physiological underpinnings of perceptual
traits in ASD remains a significant challenge in the field. Here we show how a
recurrent neural circuit model which was optimized to perform sampling-based
inference and displays characteristic features of cortical dynamics can help
bridge this gap. The model was able to establish a mechanistic link between two
descriptive levels for ASD: a physiological level, in terms of inhibitory
dysfunction, neural variability and oscillations, and a perceptual level, in
terms of hypopriors in Bayesian computations. We took two parallel paths:
inducing hypopriors in the probabilistic model, and an inhibitory dysfunction
in the network model, which lead to consistent results in terms of the
represented posteriors, providing support for the view that both descriptions
might constitute two sides of the same coin.
