EARS-UDE: Evaluating Auditory Response in Sensory Overload with Universal Differential Equations

TL;DR

This paper introduces a novel Universal Differential Equations framework combining mechanistic models and neural networks to better understand and predict auditory sensory overload in autism, improving accuracy and interpretability.

Contribution

The study presents a new UDE-based model that captures individual variability and physiological parameters in autism-related sensory overload, outperforming existing neural ODEs.

Findings

Achieved 90.8% improvement over neural ODEs

Reduced model parameters by 73.5%

Accurately recovered physiological parameters within 2% error

Abstract

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.