Homogeneous Intrinsic Neuronal Excitability Induces Overfitting to Sensory Noise: A Robot Model of Neurodevelopmental Disorder
Homogeneous Intrinsic Neuronal Excitability Induces Overfitting to Sensory Noise: A Robot Model of Neurodevelopmental Disorder
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DOI：
10.3389/fpsyt.2020.00762
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发表时间：
2020-08-12
影响因子：
4.7
通讯作者：
Ogata, Tetsuya
中科院分区：
文献类型：
--
作者：
Idei, Hayato;Murata, Shingo;Ogata, Tetsuya
神经发育障碍，包括自闭症谱系障碍，已经在神经、认知和行为层面进行了深入研究，但积累的知识仍然支离破碎。特别是，症状的发展学习方面以及与物理环境的相互作用在计算模型研究中仍然很大程度上未被探索，尽管领先的计算理论已经提出了精神症状和信息不确定性（精度）的不寻常估计之间的关联，这是现实世界的一个重要方面，并通过学习过程进行估计。在这里，我们提出了一种机械解释，通过分层预测编码和发展学习框架统一不同的观察结果，这在使用神经网络控制机器人的实验中得到了证明。结果表明，通过发展学习过程，神经水平上的同质内在神经元兴奋性通过信息处理水平上的自组织变化引起，例如感觉精度过高和对感觉噪声的过度拟合。这些变化导致了行为层面的多方面改变，例如缺乏灵活性、概括能力下降和运动笨拙。此外，这些行为改变还伴随着神经活动的波动和突触连接的过度发展。这些发现可能会弥合对自闭症谱系和其他神经发育障碍的不同层次的理解，并提供对个体患者观察到的行为和大脑活动背后的疾病过程的见解。这项研究展示了神经机器人框架在模拟大脑、身体和不确定环境之间的动态相互作用如何产生精神疾病方面的潜力。
Neurodevelopmental disorders, including autism spectrum disorder, have been intensively investigated at the neural, cognitive, and behavioral levels, but the accumulated knowledge remains fragmented. In particular, developmental learning aspects of symptoms and interactions with the physical environment remain largely unexplored in computational modeling studies, although a leading computational theory has posited associations between psychiatric symptoms and an unusual estimation of information uncertainty (precision), which is an essential aspect of the real world and is estimated through learning processes. Here, we propose a mechanistic explanation that unifies the disparate observationsviaa hierarchical predictive coding and developmental learning framework, which is demonstrated in experiments using a neural network-controlled robot. The results show that, through the developmental learning process, homogeneous intrinsic neuronal excitability at the neural level inducedviaself-organization changes at the information processing level, such as hyper sensory precision and overfitting to sensory noise. These changes led to multifaceted alterations at the behavioral level, such as inflexibility, reduced generalization, and motor clumsiness. In addition, these behavioral alterations were accompanied by fluctuating neural activity and excessive development of synaptic connections. These findings might bridge various levels of understandings in autism spectrum and other neurodevelopmental disorders and provide insights into the disease processes underlying observed behaviors and brain activities in individual patients. This study shows the potential of neurorobotics frameworks for modeling how psychiatric disorders arise from dynamic interactions among the brain, body, and uncertain environments.