Bridging physiological and perceptual views of autism by means of sampling-based Bayesian inference.

ABSTRACT: 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 that 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 probabi

SUBMITTER: Echeveste R 

PROVIDER: S-EPMC9810278 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature