Energetics based spike generation of a single neuron: simulation results and analysis.

Frontiers in neuroenergetics Pub Date : 2012-02-01 eCollection Date: 2012-01-01 DOI:10.3389/fnene.2012.00002
Nagarajan Venkateswaran, Sudarshan Sekhar, Thiagarajan Thirupatchur Sanjayasarathy, Sharath Navalpakkam Krishnan, Dinesh Kannan Kabaleeswaran, Subbu Ramanathan, Narendran Narayanasamy, Sharan Srinivas Jagathrakshakan, S R Vignesh
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引用次数: 8

Abstract

Existing current based models that capture spike activity, though useful in studying information processing capabilities of neurons, fail to throw light on their internal functioning. It is imperative to develop a model that captures the spike train of a neuron as a function of its intracellular parameters for non-invasive diagnosis of diseased neurons. This is the first ever article to present such an integrated model that quantifies the inter-dependency between spike activity and intracellular energetics. The generated spike trains from our integrated model will throw greater light on the intracellular energetics than existing current models. Now, an abnormality in the spike of a diseased neuron can be linked and hence effectively analyzed at the energetics level. The spectral analysis of the generated spike trains in a time-frequency domain will help identify abnormalities in the internals of a neuron. As a case study, the parameters of our model are tuned for Alzheimer's disease and its resultant spike trains are studied and presented. This massive initiative ultimately aims to encompass the entire molecular signaling pathways of the neuronal bioenergetics linking it to the voltage spike initiation and propagation; due to the lack of experimental data quantifying the inter dependencies among the parameters, the model at this stage adopts a particular level of functionality and is shown as an approach to study and perform disease modeling at the spike train and the mitochondrial bioenergetics level.

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基于能量学的单神经元脉冲生成:仿真结果与分析。
现有的基于当前的捕捉脉冲活动的模型,虽然在研究神经元的信息处理能力方面很有用,但却无法阐明它们的内部功能。建立一种模型,捕捉神经元的尖峰序列作为其细胞内参数的函数,对病变神经元进行非侵入性诊断是必要的。这是有史以来第一次提出这样一个综合模型,量化尖峰活动和细胞内能量学之间的相互依赖性。从我们的综合模型中产生的尖峰序列将比现有的模型更能揭示细胞内能量学。现在,病变神经元的尖峰异常可以联系起来,从而在能量水平上有效地分析。在时频域对产生的尖峰序列进行频谱分析将有助于识别神经元内部的异常。作为一个案例研究,我们的模型参数调整为阿尔茨海默病和其产生的尖峰序列进行了研究和介绍。这项大规模的倡议最终旨在涵盖神经元生物能量学的整个分子信号通路,将其与电压尖峰的起始和传播联系起来;由于缺乏量化参数之间相互依赖关系的实验数据,该模型在此阶段采用了特定水平的功能,并被证明是在刺突序列和线粒体生物能量学水平上研究和进行疾病建模的方法。
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