Individuals diagnosed with autism spectrum disorder (ASD) show neural and behavioral characteristics differing from the neurotypical population. This may stem from a computational principle that relates inference and computational dynamics to the dynamic range of neuronal population responses, reflecting the signal levels for which the system is responsive. In the present study, we showed that an increased dynamic range (IDR), indicating a gradual response of a neuronal population to changes in input, accounts for neural and behavioral variations in individuals diagnosed with ASD across diverse tasks. We validated the model with data from finger-tapping synchronization, orientation reproduction and global motion coherence tasks. We suggested that increased heterogeneity in the half-activation point of individual neurons may be the biological mechanism underlying the IDR in ASD. Taken together, this model provides a proof of concept for a new computational principle that may account for ASD and generates new testable and distinct predictions regarding its behavioral, neural and biological foundations.

被诊断患有自闭症谱系障碍 （ASD） 的个体表现出与神经典型人群不同的神经和行为特征。这可能源于一种计算原理，该原理将推理和计算动力学与神经元群反应的动态范围联系起来，反映了系统响应的信号水平。在本研究中，我们表明动态范围增加 （IDR），表明神经元群对输入变化的逐渐反应，解释了被诊断患有 ASD 的个体在不同任务中的神经和行为变化。我们使用来自手指敲击同步、方向再现和全局运动相干任务的数据验证了该模型。我们认为，单个神经元半激活点的异质性增加可能是 ASD 中 IDR 的生物学机制。综上所述，该模型为一种新的计算原理提供了概念验证，该原理可以解释 ASD，并生成有关其行为、神经和生物学基础的新的可测试且独特的预测。