Integrated information theory reveals the potential role of the posterior parietal cortex in sustaining conditioning responses in classical conditioning tasks.

IF 3.2 3区 医学 Q2 NEUROSCIENCES Frontiers in Neuroscience Pub Date : 2025-01-29 eCollection Date: 2025-01-01 DOI:10.3389/fnins.2025.1512724
Tien Cuong Phi, Shin Ishii, Masashi Kondo, Masanori Matsuzaki, Ken Nakae
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Abstract

Classical conditioning is a fundamental associative learning process in which repeated pairings of a conditioned stimulus (CS) with an unconditioned stimulus (US) lead to the CS eliciting a conditioned response (CR). Previous research has identified key neural regions involved in processing reward-predicting cues and mediating licking behavior. However, the mechanisms that sustain high conditioned response rates across repeated sessions remain elusive, particularly regarding how the reward expectation is represented on a session-by-session basis. While early learning phases in classical conditioning have been extensively studied, the neural mechanisms that support consistent performance over time remain unclear. In this study, we sought to understand how cortical regions, particularly the posterior parietal cortex (PPC), contribute to maintaining high CR rates across sessions. Using the core complex framework derived from Integrated Information Theory (IIT), we explored the dynamics of neural networks during sessions of high CR performance. Our findings suggest that while traditional functional connectivity (FC) methods struggled to capture the complexity of sustained behavioral engagement, the core complex framework revealed key regions, notably the PPC, that were significantly correlated with enhanced CR sessions. This work suggests the potential role of the PPC in supporting reward expectations and maintaining consistent behavioral responses. By applying the core complex framework to investigate neural substrates of sustained behavior, we provide novel insights into the interaction of cortical networks during classical conditioning, offering promising directions for future research in associative learning and behavior.

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综合信息理论揭示了后顶叶皮层在经典条件反射任务中维持条件反射反应中的潜在作用。
经典条件反射是一个基本的联想学习过程,在这个过程中,条件刺激与非条件刺激的重复配对导致条件刺激引发条件反应。先前的研究已经确定了处理奖励预测线索和调节舔舐行为的关键神经区域。然而,在重复的会话中维持高条件反应率的机制仍然是难以捉摸的,特别是关于奖励期望是如何在每个会话的基础上表现出来的。虽然经典条件反射的早期学习阶段已被广泛研究,但支持长期一致表现的神经机制仍不清楚。在这项研究中,我们试图了解皮层区域,特别是后顶叶皮层(PPC)是如何在整个过程中维持高CR率的。利用源自集成信息理论(IIT)的核心复杂框架,我们探索了神经网络在高CR表现期间的动态。我们的研究结果表明,虽然传统的功能连接(FC)方法难以捕捉持续行为参与的复杂性,但核心复杂框架揭示了关键区域,特别是PPC,与增强的CR会话显著相关。这项工作表明PPC在支持奖励预期和维持一致的行为反应方面的潜在作用。通过应用核心复杂框架来研究持续行为的神经基础,我们为经典条件反射过程中皮层网络的相互作用提供了新的见解,为联想学习和行为的未来研究提供了有希望的方向。
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来源期刊
Frontiers in Neuroscience
Frontiers in Neuroscience NEUROSCIENCES-
CiteScore
6.20
自引率
4.70%
发文量
2070
审稿时长
14 weeks
期刊介绍: Neural Technology is devoted to the convergence between neurobiology and quantum-, nano- and micro-sciences. In our vision, this interdisciplinary approach should go beyond the technological development of sophisticated methods and should contribute in generating a genuine change in our discipline.
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