Trapani Alessandra Maria, Sartori Carlo Andrea, Gambosi Benedetta, Pedrocchi Alessandra, Antonietti Alberto
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy.
APL Bioeng. 2025 Jun 12;9(2):026125. doi: 10.1063/5.0250953. eCollection 2025 Jun.
Nitric oxide (NO) is a versatile signaling molecule with significant roles in various physiological processes, including synaptic plasticity and memory formation. In the cerebellum, NO is produced by neural NO synthase and diffuses to influence synaptic changes, particularly at parallel fiber-Purkinje cell synapses. This study aims to investigate NO's role in cerebellar learning mechanisms using a biologically realistic simulation-based approach. We developed the NO Diffusion Simulator (NODS), a Python module designed to model NO production and diffusion within a cerebellar spiking neural network framework. Our simulations focus on the eye-blink classical conditioning protocol to assess the impact of NO modulation on long-term potentiation and depression at parallel fiber-Purkinje cell synapses. The results demonstrate that NO diffusion significantly affects synaptic plasticity, dynamically adjusting learning rates based on synaptic activity patterns. This metaplasticity mechanism enhances the cerebellum's capacity to prioritize relevant inputs and mitigate learning interference, selectively modulating synaptic efficacy. Our findings align with theoretical models, suggesting that NO serves as a contextual indicator, optimizing learning rates for effective motor control and adaptation to new tasks. The NODS implementation provides an efficient tool for large-scale simulations, facilitating future studies on NO dynamics in various brain regions and neurovascular coupling scenarios. By bridging the gap between molecular processes and network-level learning, this work underscores the critical role of NO in cerebellar function and offers a robust framework for exploring NO-dependent plasticity in computational neuroscience.
一氧化氮（NO）是一种多功能信号分子，在包括突触可塑性和记忆形成在内的各种生理过程中发挥着重要作用。在小脑中，NO由神经元型一氧化氮合酶产生并扩散，以影响突触变化，特别是在平行纤维-浦肯野细胞突触处。本研究旨在使用基于生物现实模拟的方法研究NO在小脑学习机制中的作用。我们开发了NO扩散模拟器（NODS），这是一个用Python编写的模块，旨在模拟小脑中脉冲神经网络框架内NO的产生和扩散。我们的模拟聚焦于眨眼经典条件反射实验方案，以评估NO调节对平行纤维-浦肯野细胞突触处长期增强和抑制的影响。结果表明，NO扩散显著影响突触可塑性，根据突触活动模式动态调整学习率。这种元可塑性机制增强了小脑区分相关输入并减轻学习干扰的能力，选择性地调节突触效能。我们的研究结果与理论模型一致，表明NO作为一种情境指标，优化学习率以实现有效的运动控制和适应新任务。NODS的实现为大规模模拟提供了一个有效的工具，便于未来对不同脑区和神经血管耦合场景下的NO动力学进行研究。通过弥合分子过程与网络水平学习之间的差距，这项工作强调了NO在小脑功能中的关键作用，并为在计算神经科学中探索NO依赖性可塑性提供了一个强大的框架。
