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

Abstract

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.

Digital Twin Brain Simulator: Harnessing Primate ECoG Data for Real-Time Consciousness Monitoring and Virtual Intervention

“…This theory hypothesizes that the brain's information processing is analogous to data assimilation. Indeed, similar RNN models have been utilized in computational modeling and neurodevelopmental robotics research to understand cortical information processing, cognitive functions, and psychiatric symptoms [31][32][33][34][35]. The present research may be interpreted as demonstrating that the free energy principle-based approach is beneficial for simulating not only cognitive processes, but also brain signals.…”