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

TL;DR

This paper presents a neural circuit model that links physiological features like inhibitory dysfunction to perceptual traits in autism spectrum disorder through sampling-based Bayesian inference, offering a unified mechanistic understanding.

Contribution

It introduces a recurrent neural network model optimized for sampling-based inference that connects physiological and perceptual descriptions of ASD, bridging a significant gap in understanding.

Findings

Model links inhibitory dysfunction to hypopriors in Bayesian inference.

Consistent results between physiological and perceptual manipulations.

Supports unified view of ASD at physiological and perceptual levels.

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 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.