Department of Intermedia Studies, Waseda University, Tokyo, 169-8555, Japan.
Department of Electronics and Electrical Engineering, Keio University, Kanagawa, 223-8522, Japan.
Neural Netw. 2021 Jun;138:150-163. doi: 10.1016/j.neunet.2021.01.033. Epub 2021 Feb 12.
Neurodevelopmental disorders are characterized by heterogeneous and non-specific nature of their clinical symptoms. In particular, hyper- and hypo-reactivity to sensory stimuli are diagnostic features of autism spectrum disorder and are reported across many neurodevelopmental disorders. However, computational mechanisms underlying the unusual paradoxical behaviors remain unclear. In this study, using a robot controlled by a hierarchical recurrent neural network model with predictive processing and learning mechanism, we simulated how functional disconnection altered the learning process and subsequent behavioral reactivity to environmental change. The results show that, through the learning process, long-range functional disconnection between distinct network levels could simultaneously lower the precision of sensory information and higher-level prediction. The alteration caused a robot to exhibit sensory-dominated and sensory-ignoring behaviors ascribed to sensory hyper- and hypo-reactivity, respectively. As long-range functional disconnection became more severe, a frequency shift from hyporeactivity to hyperreactivity was observed, paralleling an early sign of autism spectrum disorder. Furthermore, local functional disconnection at the level of sensory processing similarly induced hyporeactivity due to low sensory precision. These findings suggest a computational explanation for paradoxical sensory behaviors in neurodevelopmental disorders, such as coexisting hyper- and hypo-reactivity to sensory stimulus. A neurorobotics approach may be useful for bridging various levels of understanding in neurodevelopmental disorders and providing insights into mechanisms underlying complex clinical symptoms.
神经发育障碍的临床症状具有异质性和非特异性。特别是，对感觉刺激的过度和低度反应是自闭症谱系障碍的诊断特征，在许多神经发育障碍中都有报道。然而，异常矛盾行为的计算机制仍不清楚。在这项研究中，我们使用了一个由具有预测处理和学习机制的分层递归神经网络模型控制的机器人，模拟了功能连接中断如何改变学习过程以及随后对环境变化的行为反应。结果表明，通过学习过程，不同网络层次之间的长程功能连接中断可以同时降低感觉信息和高级预测的精度。这种改变导致机器人表现出感觉主导和感觉忽略行为，分别归因于感觉过度和感觉低度反应。随着长程功能连接中断变得更加严重，观察到从感觉低度反应到感觉过度反应的频率转移，这与自闭症谱系障碍的早期迹象相平行。此外，感觉处理水平的局部功能连接中断也会由于感觉精度低而导致感觉低度反应。这些发现为神经发育障碍中的矛盾感觉行为提供了一种计算解释，例如对感觉刺激同时存在过度和低度反应。神经机器人学方法可能有助于弥合神经发育障碍各个理解层面的差距，并为复杂临床症状的机制提供深入了解。
