Suppr超能文献

周围神经损伤后异步不规则神经元网络修复的生长规律。

Growth rules for the repair of Asynchronous Irregular neuronal networks after peripheral lesions.

机构信息

UH Biocomputation Research Group, Centre for Computer Science and Informatics Research, University of Hertfordshire, Hatfield United Kingdom.

Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany.

出版信息

PLoS Comput Biol. 2021 Jun 1;17(6):e1008996. doi: 10.1371/journal.pcbi.1008996. eCollection 2021 Jun.

Abstract

Several homeostatic mechanisms enable the brain to maintain desired levels of neuronal activity. One of these, homeostatic structural plasticity, has been reported to restore activity in networks disrupted by peripheral lesions by altering their neuronal connectivity. While multiple lesion experiments have studied the changes in neurite morphology that underlie modifications of synapses in these networks, the underlying mechanisms that drive these changes are yet to be explained. Evidence suggests that neuronal activity modulates neurite morphology and may stimulate neurites to selective sprout or retract to restore network activity levels. We developed a new spiking network model of peripheral lesioning and accurately reproduced the characteristics of network repair after deafferentation that are reported in experiments to study the activity dependent growth regimes of neurites. To ensure that our simulations closely resemble the behaviour of networks in the brain, we model deafferentation in a biologically realistic balanced network model that exhibits low frequency Asynchronous Irregular (AI) activity as observed in cerebral cortex. Our simulation results indicate that the re-establishment of activity in neurons both within and outside the deprived region, the Lesion Projection Zone (LPZ), requires opposite activity dependent growth rules for excitatory and inhibitory post-synaptic elements. Analysis of these growth regimes indicates that they also contribute to the maintenance of activity levels in individual neurons. Furthermore, in our model, the directional formation of synapses that is observed in experiments requires that pre-synaptic excitatory and inhibitory elements also follow opposite growth rules. Lastly, we observe that our proposed structural plasticity growth rules and the inhibitory synaptic plasticity mechanism that also balances our AI network both contribute to the restoration of the network to pre-deafferentation stable activity levels.

摘要

几种体内平衡机制使大脑能够维持神经元活动的理想水平。其中一种,即体内平衡结构可塑性,据报道可以通过改变神经元连接来恢复因外周损伤而中断的网络中的活动。虽然多项损伤实验研究了神经突形态的变化,这些变化是网络中突触修饰的基础,但驱动这些变化的潜在机制仍有待解释。有证据表明,神经元活动调节神经突形态,并可能刺激神经突选择性发芽或缩回,以恢复网络活动水平。我们开发了一个新的尖峰网络模型,用于研究外周损伤,该模型准确地再现了去传入后网络修复的特征,这些特征在研究神经突活动依赖性生长模式的实验中有所报道。为了确保我们的模拟与大脑中网络的行为非常相似,我们在一个表现出大脑皮层中观察到的低频异步不规则(AI)活动的生物现实平衡网络模型中模拟去传入。我们的模拟结果表明,在剥夺区域(LPZ)内外神经元活动的重新建立,需要兴奋性和抑制性突触后元件的相反的活动依赖性生长规则。对这些生长模式的分析表明,它们也有助于维持单个神经元的活动水平。此外,在我们的模型中,实验中观察到的突触的定向形成要求突触前兴奋性和抑制性元件也遵循相反的生长规则。最后,我们观察到,我们提出的结构可塑性生长规则和平衡我们的 AI 网络的抑制性突触可塑性机制都有助于将网络恢复到去传入前的稳定活动水平。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4dbd/8195387/ec6a6db37f32/pcbi.1008996.g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验