用通用微分方程建模自闭症听觉过载，兼顾生理机制与个体差异。
EARS-UDE: Evaluating Auditory Response in Sensory Overload with Universal Differential Equations
- 结合生物物理模型与神经网络，构建可解释的动态响应模型。
- 相比纯神经微分方程，性能提升90.8%，参数减少73.5%。
- 能精准预测特定刺激下的17.2%过载风险，适合临床与可穿戴设备。
听觉感官过载影响50%-70%自闭症谱系障碍（ASD）患者。现有方法如机理模型（霍奇金-赫胥黎型、威尔逊-科万、兴奋抑制平衡）、临床工具（EEG/MEG、感官量表）及机器学习方法（神经微分方程、预测编码），或假设参数固定，或缺乏可解释性，难以捕捉自闭症的异质性。本文提出一种科学机器学习框架，采用通用微分方程（UDEs）建模自闭症中的感官适应动态。该框架融合基于生物物理的常微分方程与神经网络，兼顾机制理解与个体差异。实验表明，UDEs相较纯神经微分方程性能提升90.8%，同时参数量减少73.5%。模型成功将生理参数恢复至2%误差内，并可量化评估感官过载风险，对具有特定时序模式的脉冲刺激，预测17.2%的过载风险。该框架为自闭症的个性化、循证干预奠定基础，可直接应用于可穿戴设备与临床实践。
原文摘要 · Abstract (English)
Auditory sensory overload affects 50-70% of individuals with Autism Spectrum Disorder (ASD), yet existing approaches, such as mechanistic models (Hodgkin Huxley type, Wilson Cowan, excitation inhibition balance), clinical tools (EEG/MEG, Sensory Profile scales), and ML methods (Neural ODEs, predictive coding), either assume fixed parameters or lack interpretability, missing autism heterogeneity. We present a Scientific Machine Learning approach using Universal Differential Equations (UDEs) to model sensory adaptation dynamics in autism. Our framework combines ordinary differential equations grounded in biophysics with neural networks to capture both mechanistic understanding and individual variability. We demonstrate that UDEs achieve a 90.8% improvement over pure Neural ODEs while using 73.5% fewer parameters. The model successfully recovers physiological parameters within the 2% error and provides a quantitative risk assessment for sensory overload, predicting 17.2% risk for pulse stimuli with specific temporal patterns. This framework establishes foundations for personalized, evidence-based interventions in autism, with direct applications to wearable technology and clinical practice.
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