Modeling nitric oxide diffusion and plasticity modulation in cerebellar learning.

IF 6.6 3区 医学 Q1 ENGINEERING, BIOMEDICAL
APL Bioengineering Pub Date : 2025-06-12 eCollection Date: 2025-06-01 DOI:10.1063/5.0250953
Alessandra Maria Trapani, Carlo Andrea Sartori, Benedetta Gambosi, Alessandra Pedrocchi, Alberto Antonietti
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引用次数: 0

Abstract

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.

模拟小脑学习中的一氧化氮扩散和可塑性调节。
一氧化氮(NO)是一种多功能信号分子,在突触可塑性和记忆形成等多种生理过程中发挥重要作用。在小脑中,NO由神经NO合酶产生并扩散影响突触的变化,特别是在平行纤维-浦肯野细胞突触。本研究旨在探讨一氧化氮在小脑学习机制中的作用,采用基于生物学现实模拟的方法。我们开发了NO扩散模拟器(NODS),这是一个Python模块,旨在模拟小脑尖峰神经网络框架内NO的产生和扩散。我们的模拟集中在眨眼经典条件反射方案上,以评估NO调节对平行纤维-浦肯野细胞突触的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节学习率。这种元可塑性机制增强了小脑优先考虑相关输入和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可以作为一种情境指标,优化学习率,从而有效地控制运动并适应新任务。NODS的实现为大规模模拟提供了有效的工具,为未来研究不同脑区和神经血管耦合情景中的NO动力学提供了便利。通过弥合分子过程和网络级学习之间的差距,这项工作强调了NO在小脑功能中的关键作用,并为探索计算神经科学中NO依赖的可塑性提供了一个强大的框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
APL Bioengineering
APL Bioengineering ENGINEERING, BIOMEDICAL-
CiteScore
9.30
自引率
6.70%
发文量
39
审稿时长
19 weeks
期刊介绍: APL Bioengineering is devoted to research at the intersection of biology, physics, and engineering. The journal publishes high-impact manuscripts specific to the understanding and advancement of physics and engineering of biological systems. APL Bioengineering is the new home for the bioengineering and biomedical research communities. APL Bioengineering publishes original research articles, reviews, and perspectives. Topical coverage includes: -Biofabrication and Bioprinting -Biomedical Materials, Sensors, and Imaging -Engineered Living Systems -Cell and Tissue Engineering -Regenerative Medicine -Molecular, Cell, and Tissue Biomechanics -Systems Biology and Computational Biology
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