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<p>Individuals with Autism Spectrum Disorder (ASD) are known for their socio-communicative challenges, including face recognition. It is unclear, however, about its neurocomputational basis and links to neurobiological factors, such as an imbalance of excitatory and inhibitory signals (E/I imbalance) or excessive internal noise (IN). This study employed Convolutional Neural Network (CNN) models to simulate face recognition in typical populations and ASD based on the claims of E/I imbalance and IN theories. We demonstrated that CNN models with non-optimal ReLU slopes or noisy activations yielded poorer performance in face recognition and exhibited atypical neural representations of faces. Overall, simulations based on the E/I imbalance theory seem to encompass a broader range of behavioral and neural&#xa0;profiles in ASD. Our theory-driven approach used CNN models to test neurobiological theories, and our results provide causal evidence on a potential mechanism by which neurobiological factors influence face recognition in ASD.</p>

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E/I imbalance and internal noise cause weak neural representations and face recognition challenges in ASD

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月24日

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  • Xijing Wang,
  • Emily Rios,
  • Lang Chen

摘要

Individuals with Autism Spectrum Disorder (ASD) are known for their socio-communicative challenges, including face recognition. It is unclear, however, about its neurocomputational basis and links to neurobiological factors, such as an imbalance of excitatory and inhibitory signals (E/I imbalance) or excessive internal noise (IN). This study employed Convolutional Neural Network (CNN) models to simulate face recognition in typical populations and ASD based on the claims of E/I imbalance and IN theories. We demonstrated that CNN models with non-optimal ReLU slopes or noisy activations yielded poorer performance in face recognition and exhibited atypical neural representations of faces. Overall, simulations based on the E/I imbalance theory seem to encompass a broader range of behavioral and neural profiles in ASD. Our theory-driven approach used CNN models to test neurobiological theories, and our results provide causal evidence on a potential mechanism by which neurobiological factors influence face recognition in ASD.