Simulating developmental diversity: Impact of neural stochasticity on atypical flexibility and hierarchy

Frontiers in Psychiatry

Psychiatry

Jun Tani¹  Manabu Honda²  Yuichi Yamashita²  Takafumi Soda³  Ahmadreza Ahmadi⁴  Takashi Hanakawa⁵ 

[1] Cognitive Neurorobotics Research Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan;Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan;Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan;Department of NCNP Brain Physiology and Pathology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan;Geobotica, Brisbane, QLD, Australia;Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Kyoto, Japan;

关键词: autism spectrum disorder (ASD);    computational psychiatry;    predictive coding;    flexibility;    representation learning;    neural noise;    Bayesian brain;    neural network;   

DOI  :  10.3389/fpsyt.2023.1080668

 received in 2022-10-26, accepted in 2023-02-21,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionInvestigating the pathological mechanisms of developmental disorders is a challenge because the symptoms are a result of complex and dynamic factors such as neural networks, cognitive behavior, environment, and developmental learning. Recently, computational methods have started to provide a unified framework for understanding developmental disorders, enabling us to describe the interactions among those multiple factors underlying symptoms. However, this approach is still limited because most studies to date have focused on cross-sectional task performance and lacked the perspectives of developmental learning. Here, we proposed a new research method for understanding the mechanisms of the acquisition and its failures in hierarchical Bayesian representations using a state-of-the-art computational model, referred to as in silico neurodevelopment framework for atypical representation learning.MethodsSimple simulation experiments were conducted using the proposed framework to examine whether manipulating the neural stochasticity and noise levels in external environments during the learning process can lead to the altered acquisition of hierarchical Bayesian representation and reduced flexibility.ResultsNetworks with normal neural stochasticity acquired hierarchical representations that reflected the underlying probabilistic structures in the environment, including higher-order representation, and exhibited good behavioral and cognitive flexibility. When the neural stochasticity was high during learning, top-down generation using higher-order representation became atypical, although the flexibility did not differ from that of the normal stochasticity settings. However, when the neural stochasticity was low in the learning process, the networks demonstrated reduced flexibility and altered hierarchical representation. Notably, this altered acquisition of higher-order representation and flexibility was ameliorated by increasing the level of noises in external stimuli.DiscussionThese results demonstrated that the proposed method assists in modeling developmental disorders by bridging between multiple factors, such as the inherent characteristics of neural dynamics, acquisitions of hierarchical representation, flexible behavior, and external environment.

【 授权许可】

Unknown   
Copyright © 2023 Soda, Ahmadi, Tani, Honda, Hanakawa and Yamashita.